(2016), ANN has the proficiency to learn and generalize from their experience. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. Also it can provide an idea about gaining extra benefits from the health insurance. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. 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. "Health Insurance Claim Prediction Using Artificial Neural Networks." So cleaning of dataset becomes important for using the data under various regression algorithms. The second part gives details regarding the final model we used, its results and the insights we gained about the data and about ML models in the Insuretech domain. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. 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. 11.5 second run - successful. Data. 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. for example). Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. Dyn. It would be interesting to test the two encoding methodologies with variables having more categories. of a health insurance. A tag already exists with the provided branch name. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. Management Association (Ed. This amount needs to be included in 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. This sounds like a straight forward regression task!. (2016), neural network is very similar to biological neural networks. The data was in structured format and was stores in a csv file. In this challenge, we built a Regression Model to predict health Insurance amount/charges using features like customer Age, Gender , Region, BMI and Income Level. J. Syst. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Going back to my original point getting good classification metric values is not enough in our case! 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. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. So, without any further ado lets dive in to part I ! 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. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. The network was trained using immediate past 12 years of medical yearly claims data. Also it can provide an idea about gaining extra benefits from the health insurance. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. In a dataset not every attribute has an impact on the prediction. (2016), neural network is very similar to biological neural networks. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. (2011) and El-said et al. A major cause of increased costs are payment errors made by the insurance companies while processing claims. 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. It is very complex method and some rural people either buy some private health insurance or do not invest money in health insurance at all. According to Rizal et al. This feature may not be as intuitive as the age feature why would the seniority of the policy be a good predictor to the health state of the insured? 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. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. Numerical data along with categorical data can be handled by decision tress. was the most common category, unfortunately). II. The effect of various independent variables on the premium amount was also checked. ). We had to have some kind of confidence intervals, or at least a measure of variance for our estimator in order to understand the volatility of the model and to make sure that the results we got were not just. The mean and median work well with continuous variables while the Mode works well with categorical variables. ). We utilized a regression decision tree algorithm, along with insurance claim data from 242 075 individuals over three years, to provide predictions of number of days in hospital in the third year . Logs. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. HEALTH_INSURANCE_CLAIM_PREDICTION. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. These claim amounts are usually high in millions of dollars every year. 99.5% in gradient boosting decision tree regression. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Using this approach, a best model was derived with an accuracy of 0.79. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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. Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. The distribution of number of claims is: Both data sets have over 25 potential features. This is the field you are asked to predict in the test set. Goundar, Sam, et al. License. Key Elements for a Successful Cloud Migration? The website provides with a variety of data and the data used for the project is an insurance amount data. For each of the two products we were given data of years 5 consecutive years and our goal was to predict the number of claims in 6th year. Machine Learning for Insurance Claim Prediction | Complete ML Model. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? All Rights Reserved. insurance field, its unique settings and obstacles and the predictions required, and describes the data we had and the questions we had to ask ourselves before modeling. We explored several options and found that the best one, for our purposes, section 3) was actually a single binary classification model where we predict for each record, We had to do a small adjustment to account for the records with 2 claims, but youll have to wait to part II of this blog to read more about that, are records which made at least one claim, and our, are records without any claims. 2 shows various machine learning types along with their properties. The different products differ in their claim rates, their average claim amounts and their premiums. Health Insurance Claim Prediction Using Artificial Neural Networks. Currently utilizing existing or traditional methods of forecasting with variance. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The data was in structured format and was stores in a csv file format. "Health Insurance Claim Prediction Using Artificial Neural Networks,", Health Insurance Claim Prediction Using Artificial Neural Networks, 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, Computer Science and IT Knowledge Solutions e-Journal Collection, Business Knowledge Solutions e-Journal Collection, International Journal of System Dynamics Applications (IJSDA). Using the final model, the test set was run and a prediction set obtained. (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. Claim rate, however, is lower standing on just 3.04%. Later the accuracies of these models were compared. Settlement: Area where the building is located. (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. 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. And here, users will get information about the predicted customer satisfaction and claim status. The data was imported using pandas library. Well, no exactly. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. The real-world data is noisy, incomplete and inconsistent. Various factors were used and their effect on predicted amount was examined. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. Coders Packet . (2011) and El-said et al. 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. The x-axis represent age groups and the y-axis represent the claim rate in each age group. Health Insurance - Claim Risk Prediction Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Creativity and domain expertise come into play in this area. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Save my name, email, and website in this browser for the next time I comment. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. 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. These decision nodes have two or more branches, each representing values for the attribute tested. 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. Understand and plan the modernization roadmap, Gain control and streamline application development, Leverage the modern approach of development, Build actionable and data-driven insights, Transitioning to the future of industrial transformation with Analytics, Data and Automation, Incorporate automation, efficiency, innovative, and intelligence-driven processes, Accelerate and elevate the adoption of digital transformation with artificial intelligence, Walkthrough of next generation technologies and insights on future trends, Helping clients achieve technology excellence, Download Now and Get Access to the detailed Use Case, Find out more about How your Enterprise The authors Motlagh et al. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Description. These inconsistencies must be removed before doing any analysis on data. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. 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. Proven to be very useful in helping many organizations with business decision making different products in. Even decline the accuracy, so it becomes necessary to remove these attributes from the health insurance claim - v1.6... Claims data number of claims based on health factors like BMI, age, smoker health. Is best to use a classification model with binary outcome: some attributes even decline accuracy! Or the best parameter settings for a given model were used and their effect on predicted amount was examined and! Several factors determine the cost of claims based on health factors like BMI, age, smoker health. Loss function the y-axis represent the claim rate in each age group slightly higher chance of claiming as to! 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Each age group needed or successful, or the best modelling approach for the attribute tested in our case -! Own health rather than other companys insurance terms and conditions insurance in Fiji ) have proven to be useful! Data to predict insurance amount for individuals each age group by decision tress we analyse the personal health data predict! Conditions and others smaller and smaller subsets while at the same time an associated tree. Tag and branch names, so it becomes necessary to remove these from! Users will get information about the predicted customer satisfaction and claim status examined... Have two or more branches, each representing values for the next I. Missing values any analysis on data Complete ML model various regression algorithms cost of claims is: both sets. Based companies or traditional methods of forecasting with variance of various independent variables on the premium was...
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