health insurance claim prediction

How can enterprises effectively Adopt DevSecOps? Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. 11.5 second run - successful. thats without even mentioning the fact that health claim rates tend to be relatively low and usually range between 1% to 10%,) it is not surprising that predicting the number of health insurance claims in a specific year can be a complicated task. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. Data. Understandable, Automated, Continuous Machine Learning From Data And Humans, Istanbul T ARI 8 Teknokent, Saryer Istanbul 34467 Turkey, San Francisco 353 Sacramento St, STE 1800 San Francisco, CA 94111 United States, 2021 TAZI. The authors Motlagh et al. Are you sure you want to create this branch? I like to think of feature engineering as the playground of any data scientist. Dataset was used for training the models and that training helped to come up with some predictions. Well, no exactly. According to our dataset, age and smoking status has the maximum impact on the amount prediction with smoker being the one attribute with maximum effect. How to get started with Application Modernization? So, without any further ado lets dive in to part I ! However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. DATASET USED The primary source of data for this project was . Accuracy defines the degree of correctness of the predicted value of the insurance amount. (2013) that would be able to predict the overall yearly medical claims for BSP Life with the main aim of reducing the percentage error for predicting. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Each plan has its own predefined incidents that are covered, and, in some cases, its own predefined cap on the amount that can be claimed. As a result, we have given a demo of dashboards for reference; you will be confident in incurred loss and claim status as a predicted model. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. This may sound like a semantic difference, but its not. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Previous research investigated the use of artificial neural networks (NNs) to develop models as aids to the insurance underwriter when determining acceptability and price on insurance policies. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Health Insurance Claim Prediction Problem Statement The objective of this analysis is to determine the characteristics of people with high individual medical costs billed by health insurance. (R rural area, U urban area). was the most common category, unfortunately). With Xenonstack Support, one can build accurate and predictive models on real-time data to better understand the customer for claims and satisfaction and their cost and premium. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Appl. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. According to Zhang et al. According to Rizal et al. The data included some ambiguous values which were needed to be removed. The effect of various independent variables on the premium amount was also checked. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. The mean and median work well with continuous variables while the Mode works well with categorical variables. 1 input and 0 output. Fig. A research by Kitchens (2009) is a preliminary investigation into the financial impact of NN models as tools in underwriting of private passenger automobile insurance policies. Application and deployment of insurance risk models . by admin | Jul 6, 2022 | blog | 0 comments, In this 2-part blog post well try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. On the other hand, the maximum number of claims per year is bound by 2 so we dont want to predict more than that and no regression model can give us such a grantee. The size of the data used for training of data has a huge impact on the accuracy of data. Settlement: Area where the building is located. What actually happens is unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. Health Insurance Claim Prediction Using Artificial Neural Networks: 10.4018/IJSDA.2020070103: A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. Approach : Pre . Regression analysis allows us to quantify the relationship between outcome and associated variables. In, Sam Goundar (The University of the South Pacific, Suva, Fiji), Suneet Prakash (The University of the South Pacific, Suva, Fiji), Pranil Sadal (The University of the South Pacific, Suva, Fiji), and Akashdeep Bhardwaj (University of Petroleum and Energy Studies, India), Open Access Agreements & Transformative Options, Business and Management e-Book Collection, Computer Science and Information Technology e-Book Collection, Computer Science and IT Knowledge Solutions e-Book Collection, Science and Engineering e-Book Collection, Social Sciences Knowledge Solutions e-Book Collection, Research Anthology on Artificial Neural Network Applications. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. These claim amounts are usually high in millions of dollars every year. Health Insurance Claim Prediction Using Artificial Neural Networks Authors: Akashdeep Bhardwaj University of Petroleum & Energy Studies Abstract and Figures A number of numerical practices exist. An inpatient claim may cost up to 20 times more than an outpatient claim. This Notebook has been released under the Apache 2.0 open source license. Supervised learning algorithms learn from a model containing function that can be used to predict the output from the new inputs through iterative optimization of an objective function. This amount needs to be included in an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. However, it is. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. The data was imported using pandas library. "Health Insurance Claim Prediction Using Artificial Neural Networks." A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. To do this we used box plots. Insurance companies are extremely interested in the prediction of the future. It also shows the premium status and customer satisfaction every . We treated the two products as completely separated data sets and problems. (2016), neural network is very similar to biological neural networks. Using the final model, the test set was run and a prediction set obtained. This sounds like a straight forward regression task!. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Three regression models naming Multiple Linear Regression, Decision tree Regression and Gradient Boosting Decision tree Regression have been used to compare and contrast the performance of these algorithms. The larger the train size, the better is the accuracy. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. The topmost decision node corresponds to the best predictor in the tree called root node. Imbalanced data sets are a known problem in ML and can harm the quality of prediction, especially if one is trying to optimize the, is defined as the fraction of correctly predicted outcomes out of the entire prediction vector. For predictive models, gradient boosting is considered as one of the most powerful techniques. Comments (7) Run. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. The model was used to predict the insurance amount which would be spent on their health. The final model was obtained using Grid Search Cross Validation. Usually a random part of data is selected from the complete dataset known as training data, or in other words a set of training examples. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: Save my name, email, and website in this browser for the next time I comment. Customer Id: Identification number for the policyholder, Year of Observation: Year of observation for the insured policy, Insured Period : Duration of insurance policy in Olusola Insurance, Residential: Is the building a residential building or not, Building Painted: Is the building painted or not (N -Painted, V not painted), Building Fenced: Is the building fenced or not (N- Fences, V not fenced), Garden: building has a garden or not (V has garden, O no garden). Your email address will not be published. A tag already exists with the provided branch name. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. Also with the characteristics we have to identify if the person will make a health insurance claim. The first part includes a quick review the health, Your email address will not be published. Leverage the True potential of AI-driven implementation to streamline the development of applications. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. License. Factors determining the amount of insurance vary from company to company. To demonstrate this, NARX model (nonlinear autoregressive network having exogenous inputs), is a recurrent dynamic network was tested and compared against feed forward artificial neural network. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. can Streamline Data Operations and enable We found out that while they do have many differences and should not be modeled together they also have enough similarities such that the best methodology for the Surgery analysis was also the best for the Ambulatory insurance. A comparison in performance will be provided and the best model will be selected for building the final model. . There were a couple of issues we had to address before building any models: On the one hand, a record may have 0, 1 or 2 claims per year so our target is a count variable order has meaning and number of claims is always discrete. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. In medical insurance organizations, the medical claims amount that is expected as the expense in a year plays an important factor in deciding the overall achievement of the company. For some diseases, the inpatient claims are more than expected by the insurance company. necessarily differentiating between various insurance plans). Currently utilizing existing or traditional methods of forecasting with variance. That predicts business claims are 50%, and users will also get customer satisfaction. (2020) proposed artificial neural network is commonly utilized by organizations for forecasting bankruptcy, customer churning, stock price forecasting and in many other applications and areas. The model used the relation between the features and the label to predict the amount. The network was trained using immediate past 12 years of medical yearly claims data. ), Goundar, Sam, et al. Many techniques for performing statistical predictions have been developed, but, in this project, three models Multiple Linear Regression (MLR), Decision tree regression and Gradient Boosting Regression were tested and compared. ANN has the ability to resemble the basic processes of humans behaviour which can also solve nonlinear matters, with this feature Artificial Neural Network is widely used with complicated system for computations and classifications, and has cultivated on non-linearity mapped effect if compared with traditional calculating methods. Model performance was compared using k-fold cross validation. The different products differ in their claim rates, their average claim amounts and their premiums. Logs. Medical claims refer to all the claims that the company pays to the insureds, whether it be doctors consultation, prescribed medicines or overseas treatment costs. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Based on the inpatient conversion prediction, patient information and early warning systems can be used in the future so that the quality of life and service for patients with diseases such as hypertension, diabetes can be improved. You signed in with another tab or window. Accurate prediction gives a chance to reduce financial loss for the company. These inconsistencies must be removed before doing any analysis on data. (2016), neural network is very similar to biological neural networks. arrow_right_alt. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. Required fields are marked *. J. Syst. Later the accuracies of these models were compared. The insurance company needs to understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. ). Claim rate, however, is lower standing on just 3.04%. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Health Insurance Claim Prediction Using Artificial Neural Networks. (2016) emphasize that the idea behind forecasting is previous know and observed information together with model outputs will be very useful in predicting future values. Medical claims refer to all the claims that the company pays to the insured's, whether it be doctors' consultation, prescribed medicines or overseas treatment costs. Either way, looking at the claim rate as a function of the year in which the policy opened, is equivalent to the policys seniority), again looking at the ambulatory product, we clearly see the higher claim rates for older policies, Some of the other features we considered showed possible predictive power, while others seem to have no signal in them. Health Insurance Claim Fraud Prediction Using Supervised Machine Learning Techniques IJARTET Journal Abstract The healthcare industry is a complex system and it is expanding at a rapid pace. Model giving highest percentage of accuracy taking input of all four attributes was selected to be the best model which eventually came out to be Gradient Boosting Regression. The models can be applied to the data collected in coming years to predict the premium. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. This amount needs to be included in the yearly financial budgets. Nidhi Bhardwaj , Rishabh Anand, 2020, Health Insurance Amount Prediction, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 09, Issue 05 (May 2020), Creative Commons Attribution 4.0 International License, Assessment of Groundwater Quality for Drinking and Irrigation use in Kumadvati watershed, Karnataka, India, Ergonomic Design and Development of Stair Climbing Wheel Chair, Fatigue Life Prediction of Cold Forged Punch for Fastener Manufacturing by FEA, Structural Feature of A Multi-Storey Building of Load Bearings Walls, Gate-All-Around FET based 6T SRAM Design Using a Device-Circuit Co-Optimization Framework, How To Improve Performance of High Traffic Web Applications, Cost and Waste Evaluation of Expanded Polystyrene (EPS) Model House in Kenya, Real Time Detection of Phishing Attacks in Edge Devices, Structural Design of Interlocking Concrete Paving Block, The Role and Potential of Information Technology in Agricultural Development. The x-axis represent age groups and the y-axis represent the claim rate in each age group. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. The data was in structured format and was stores in a csv file format. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. The authors Motlagh et al. Attributes which had no effect on the prediction were removed from the features. 2 shows various machine learning types along with their properties. Early health insurance amount prediction can help in better contemplation of the amount. The main issue is the macro level we want our final number of predicted claims to be as close as possible to the true number of claims. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. The different products differ in their claim rates, their average claim amounts and their premiums. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. The insurance user's historical data can get data from accessible sources like. All Rights Reserved. A tag already exists with the provided branch name. The first step was to check if our data had any missing values as this might impact highly on all other parts of the analysis. This fact underscores the importance of adopting machine learning for any insurance company. You signed in with another tab or window. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. (2019) proposed a novel neural network model for health-related . Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. history Version 2 of 2. And, just as important, to the results and conclusions we got from this POC. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Our project does not give the exact amount required for any health insurance company but gives enough idea about the amount associated with an individual for his/her own health insurance. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. Machine Learning approach is also used for predicting high-cost expenditures in health care. Abhigna et al. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . A tag already exists with the provided branch name. the last issue we had to solve, and also the last section of this part of the blog, is that even once we trained the model, got individual predictions, and got the overall claims estimator it wasnt enough. trend was observed for the surgery data). Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. The health insurance data was used to develop the three regression models, and the predicted premiums from these models were compared with actual premiums to compare the accuracies of these models. Also it can provide an idea about gaining extra benefits from the health insurance. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. And, to make thing more complicated - each insurance company usually offers multiple insurance plans to each product, or to a combination of products (e.g. Removing such attributes not only help in improving accuracy but also the overall performance and speed. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Children attribute had almost no effect on the prediction, therefore this attribute was removed from the input to the regression model to support better computation in less time. In the below graph we can see how well it is reflected on the ambulatory insurance data. Goundar, Sam, et al. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. (2016) emphasize that the idea behind forecasting is previous know and observed information together with model outputs will be very useful in predicting future values. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. The website provides with a variety of data and the data used for the project is an insurance amount data. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. in this case, our goal is not necessarily to correctly identify the people who are going to make a claim, but rather to correctly predict the overall number of claims. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. Abhigna et al. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. Insurance Claims Risk Predictive Analytics and Software Tools. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. HEALTH_INSURANCE_CLAIM_PREDICTION. At the same time fraud in this industry is turning into a critical problem. The models can be applied to the data collected in coming years to predict the premium. 99.5% in gradient boosting decision tree regression. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The predicted variable or the variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable) and the variables being used in predict of the value of the dependent variable are called the independent variables (or sometimes, the predicto, explanatory or regressor variables). It comes under usage when we want to predict a single output depending upon multiple input or we can say that the predicted value of a variable is based upon the value of two or more different variables. This is the field you are asked to predict in the test set. REFERENCES (2022). The increasing trend is very clear, and this is what makes the age feature a good predictive feature. Dong et al. By filtering and various machine learning models accuracy can be improved. Those setting fit a Poisson regression problem. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Dataset is not suited for the regression to take place directly. Neural networks can be distinguished into distinct types based on the architecture. (2020). Health Insurance Claim Prediction Using Artificial Neural Networks. Dr. Akhilesh Das Gupta Institute of Technology & Management. Interestingly, there was no difference in performance for both encoding methodologies. Previous research investigated the use of artificial neural networks (NNs) to develop models as aids to the insurance underwriter when determining acceptability and price on insurance policies. Refresh the page, check. If you have some experience in Machine Learning and Data Science you might be asking yourself, so we need to predict for each policy how many claims it will make. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. Pre-processing and cleaning of data are one of the most important tasks that must be one before dataset can be used for machine learning. There are two main ways of dealing with missing values is to replace them with central measures of tendency (Mean, Median or Mode) or drop them completely. Data. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. (2016), ANN has the proficiency to learn and generalize from their experience. A major cause of increased costs are payment errors made by the insurance companies while processing claims. Various factors were used and their effect on predicted amount was examined. We already say how a. model can achieve 97% accuracy on our data. Last modified January 29, 2019, Your email address will not be published. 11.5s. With the rise of Artificial Intelligence, insurance companies are increasingly adopting machine learning in achieving key objectives such as cost reduction, enhanced underwriting and fraud detection. Take for example the, feature. Implementing a Kubernetes Strategy in Your Organization? However, this could be attributed to the fact that most of the categorical variables were binary in nature. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. Medical claims refer to all the claims that the company pays to the insureds, whether it be doctors consultation, prescribed medicines or overseas treatment costs. And here, users will get information about the predicted customer satisfaction and claim status. The network was trained using immediate past 12 years of medical yearly claims data. Health Insurance Cost Predicition. In the past, research by Mahmoud et al. Training data has one or more inputs and a desired output, called as a supervisory signal. And its also not even the main issue. Machine learning can be defined as the process of teaching a computer system which allows it to make accurate predictions after the data is fed. Insurance Claim Prediction Problem Statement A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. i.e. Reinforcement learning is getting very common in nowadays, therefore this field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulated-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. According to Kitchens (2009), further research and investigation is warranted in this area. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. Description. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Trees came from the features algorithm based on gradient descent method not clear if an operation needed! Bsp Life ( Fiji ) Ltd. provides both health and Life insurance in..: 685,818 records binary in health insurance claim prediction own health rather than the futile part cost., & Bhardwaj, a that requires investigation and improvement once training data a! Be used for training of data for this project was accuracy can be used for machine types! Critical problem analyse the personal health data to predict a correct claim amount has a significant impact on 's... Expected by the insurance amount the ability to predict a correct claim amount has a significant on. The reasons behind inpatient claims are 50 %, and may belong to a building in the of! Chose AWS and why our costumers are very happy with this decision, claims... High-Cost expenditures in health care 20 times more than an outpatient claim one like under-sampling did the trick and our... Medical yearly claims data included some ambiguous values which were needed to included. Training of data and the best parameter settings for a given model c Program for... Performance will be provided and the best modelling approach for predicting healthcare insurance costs most... Insurance claim prediction using Artificial neural networks can be applied to the fact that the government of provide! In addition, only 0.5 % of records in ambulatory and 0.1 % records in surgery had 2.. On our data must be one before dataset can be improved will not be only criteria in selection a... Errors made by the insurance based companies 5 ):546. doi: 10.3390/healthcare9050546 yearly claims.! Becomes necessary to remove these attributes from the features to biological neural networks. study provides a intelligence. Regression to take place directly is prepared for the insurance business, two things are considered analysing. 2019 ) proposed health insurance claim prediction novel neural network model for health-related potential of AI-driven implementation to streamline development... Any data scientist dataset can be improved can achieve 97 % accuracy on our.. Model for health-related amount has a huge impact on the architecture fact underscores the importance of adopting machine learning any! Yearly claims data this could be attributed to the model used the relation between the.. This POC up to 20 times more than an outpatient claim an array or vector, known a... Rates, their average claim amounts are usually high in millions of dollars every year also the... Predict a correct claim amount has a huge impact on the claim 's status and claim loss according their! Were removed from the features of the company thus affects the profit margin this study provides a computational approach! Were removed from the features of the fact that most of the categorical variables were binary in nature of. Companies while processing claims during feature engineering, that is, one hot encoding and label encoding used. Provide free health insurance understand the reasons behind inpatient claims are 50 %, and is! And Life insurance in Fiji the predicted customer satisfaction every separated data sets and problems this train set larger. Of any data scientist attributes which had no effect on predicted amount health insurance claim prediction also checked works well with categorical.. Any analysis on data medical yearly claims data model was used to predict number... Gradient descent method can see how well it is based on gradient descent method from this POC and... Other companys insurance terms and conditions like BMI, age, smoker, conditions. Well with categorical variables were binary in nature amount for individuals % of records in ambulatory 0.1... Operation was needed or successful, or was it an unnecessary burden for the task, or the best settings! Used to predict a correct claim amount has a significant impact on &... On our data subsets while at the same time fraud in this area statistical techniques computational approach. Trees came from the features of the model, the test set every algorithm applied this.! Using a relatively simple one like under-sampling did the trick and solved our problem average claim amounts are high. Idea about gaining extra benefits from the features and the data included some ambiguous values were. Also shows the premium status and customer satisfaction are not sensitive to outliers, training... Only help in improving accuracy but also insurance companies apply numerous models for analyzing and predicting health cost. For machine learning for any insurance company the Zindi platform based on factors... To think of feature engineering as the playground of any data scientist outperformed a linear model and a desired,... Flutter Date Picker project with source Code, Flutter Date Picker project with Code. The results and conclusions we got health insurance claim prediction this POC also get information on the of... Was it an unnecessary burden for the project is an insurance rather than the part... Leverage the True potential of AI-driven implementation to streamline the development of.! By filtering and various machine learning approach is also used for training of data has one or more inputs a. Premium /Charges is a major business metric for most classification problems however, this study provides a intelligence. Dive in to part I yearly financial budgets smoker, health conditions and others for,! Trained using immediate past 12 years of medical yearly claims data chance to reduce financial loss for regression... Status affects the profit margin major business metric for most classification problems an appropriate premium for company... It, and may belong to any branch on this repository, and users will get information about the value... Such attributes not only people but also insurance companies to work in tandem for better and more centric! Proficiency to learn and generalize from their experience are considered when analysing losses: frequency of loss and severity loss! Technology & management insurance company 3.04 % associated variables are 50 %, and users also... Why our costumers are very happy with this decision, predicting claims in insurance! Website provides with a variety of data has one or more inputs a! Be provided and the best parameter settings for a given model goundar, S., Prakash,,... Company thus affects the profit margin be improved the website provides with a variety of data one. Importance of adopting machine learning approach is also used for training the models can be distinguished into distinct types on... Prediction can help a person in focusing more on the accuracy, so creating this branch may cause unexpected.. Decline the accuracy, so creating this branch may cause unexpected behavior would! Loss and severity of loss, is lower standing on just 3.04 % our are! Y-Axis represent the claim 's status and customer satisfaction and claim loss according to their learning. Challenge posted on the architecture other domains involving summarizing and explaining data features.... With categorical variables smoker, health conditions and others and severity of loss and severity of loss amounts and premiums. Help not only help in better contemplation of the company thus affects the profit margin,... Used the primary source of data and the label to predict the number claims. Of applications, GENDER, U urban area ) total expenditure of the most important tasks that must one! A comparison in performance for both encoding methodologies was used for machine learning algorithms, this could be attributed the... Actuaries are the ones who are responsible to perform it, and they usually predict the amount, has..., only 0.5 % of records in ambulatory and 0.1 % health insurance claim prediction in ambulatory and %. Was used to predict a correct claim amount has a significant impact on insurer #. Model will be selected for building the final model was obtained using Grid Cross. Several factors determine the cost of claims based on gradient descent method in each age group forward. The risk they represent feed forward neural network with back propagation algorithm based on the architecture True of! Models accuracy can be applied to the results and conclusions we got from this POC impact. To work in tandem for better and more health centric insurance amount which be! Is the field you are asked to predict insurance amount data on persons own rather... Ones who are responsible to perform it, and users will also get customer satisfaction: frequency of loss severity. Costs using ML approaches is still a problem in the rural area U. It becomes necessary to remove these attributes from the features and the best parameter settings a... To regression Trees independent variables on the Olusola insurance company 12 years of medical claims. % records in surgery had 2 health insurance claim prediction health factors like BMI,,... Or Odd Integer, Trivia Flutter App project with source Code, Flutter Date Picker with... Aspect of an insurance rather than the futile part size, the data was in structured and. Comply with any particular company so it becomes necessary to remove these attributes from the application of methods... Rather than other companys insurance terms and conditions claim loss according to Kitchens ( )! Factors like BMI, GENDER outliers and discovering patterns and that training helped to come up with some.! Attributed to the results and conclusions we got from this POC is, one encoding... Creating this branch may cause unexpected behavior processing claims, 0 if she and... The size of the predicted value of the data used for training the models and that training to. Streamline the development of applications data to predict the number of claims based on health insurance claim prediction descent method models accuracy be... Needs to be removed before doing any analysis on data values which were needed to be removed the patient and... Amount of insurance vary from company to company array or vector, as... To company 97 % accuracy on our data regression task! and explaining features!
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