Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. Other two regression models also gave good accuracies about 80% In their prediction. How can enterprises effectively Adopt DevSecOps? This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Those setting fit a Poisson regression problem. Dyn. 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. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. 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. Management Association (Ed. Are you sure you want to create this branch? It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. Bootstrapping our data and repeatedly train models on the different samples enabled us to get multiple estimators and from them to estimate the confidence interval and variance required. 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. The primary source of data for this project was from Kaggle user Dmarco. The larger the train size, the better is the accuracy. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. of a 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. In this case, we used several visualization methods to better understand our data set. Where a person can ensure that the amount he/she is going to opt is justified. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. It also shows the premium status and customer satisfaction every month, which interprets customer satisfaction as around 48%, and customers are delighted with their insurance plans. That predicts business claims are 50%, and users will also get customer satisfaction. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. 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 dataset is comprised of 1338 records with 6 attributes. However, training has to be done first with the data associated. "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). 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. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. On outlier detection and removal as well as Models sensitive (or not sensitive) to outliers, Analytics Vidhya is a community of Analytics and Data Science professionals. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. You signed in with another tab or window. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. How to get started with Application Modernization? Insurance companies apply numerous techniques for analysing and predicting health insurance costs. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. Numerical data along with categorical data can be handled by decision tress. So, in a situation like our surgery product, where claim rate is less than 3% a classifier can achieve 97% accuracy by simply predicting, to all observations! 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 . The distribution of number of claims is: Both data sets have over 25 potential features. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. 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. The full process of preparing the data, understanding it, cleaning it and generate features can easily be yet another blog post, but in this blog well have to give you the short version after many preparations we were left with those data sets. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. In the next blog well explain how we were able to achieve this goal. Fig. This thesis focuses on modeling health insurance claims of episodic, recurring health prob- lems as Markov Chains, estimating cycle length and cost, and then pricing associated health insurance . 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. In the below graph we can see how well it is reflected on the ambulatory insurance data. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. 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. Logs. Example, Sangwan et al. (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. 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 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. Early health insurance amount prediction can help in better contemplation of the amount. 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. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. The data has been imported from kaggle website. According to Kitchens (2009), further research and investigation is warranted in this area. 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. The final model was obtained using Grid Search Cross Validation. As a result, the median was chosen to replace the missing values. The models can be applied to the data collected in coming years to predict the premium. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. 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. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. 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. 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. Machine Learning approach is also used for predicting high-cost expenditures in health care. Implementing a Kubernetes Strategy in Your Organization? It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. (2016), neural network is very similar to biological neural networks. 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). According to IBM, Exploratory Data Analysis (EDA) is an approach used by data scientists to analyze data sets and summarize their main characteristics by mainly employing visualization methods. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Interestingly, there was no difference in performance for both encoding methodologies. A tag already exists with the provided branch name. Each plan has its own predefined . Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Privacy Policy & Terms and Conditions, Life Insurance Health Claim Risk Prediction, Banking Card Payments Online Fraud Detection, Finance Non Performing Loan (NPL) Prediction, Finance Stock Market Anomaly Prediction, Finance Propensity Score Prediction (Upsell/XSell), Finance Customer Retention/Churn Prediction, Retail Pharmaceutical Demand Forecasting, IOT Unsupervised Sensor Compression & Condition Monitoring, IOT Edge Condition Monitoring & Predictive Maintenance, Telco High Speed Internet Cross-Sell Prediction. Provided branch name, health conditions and others metric for most of the code analysing and predicting health insurance.! Directly increase the total expenditure of the insurance based companies, neural network ( RNN ) a of. 2009 ), further research and investigation is warranted in this case, we used several visualization to! The premium we chose AWS and why our costumers are very happy with this decision, claims! 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