health insurance claim prediction

Machine Learning for Insurance Claim Prediction | Complete ML Model. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. Are you sure you want to create this branch? So cleaning of dataset becomes important for using the data under various regression algorithms. Machine Learning approach is also used for predicting high-cost expenditures in health care. In fact, Mckinsey estimates that in Germany alone insurers could save about 500 Million Euros each year by adopting machine learning systems in healthcare insurance. The goal of this project is to allows a person to get an idea about the necessary amount required according to their own health status. 2 shows various machine learning types along with their properties. The larger the train size, the better is the accuracy. 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. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Required fields are marked *. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Factors determining the amount of insurance vary from company to company. In neural network forecasting, usually the results get very close to the true or actual values simply because this model can be iteratively be adjusted so that errors are reduced. According to Zhang et al. Then the predicted amount was compared with the actual data to test and verify the model. Goundar, Sam, et al. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. The insurance user's historical data can get data from accessible sources like. Different parameters were used to test the feed forward neural network and the best parameters were retained based on the model, which had least mean absolute percentage error (MAPE) on training data set as well as testing data set. 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. 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 In the next blog well explain how we were able to achieve this goal. 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. Maybe we should have two models first a classifier to predict if any claims are going to be made and than a classifier to determine the number of claims, or 2)? In neural network forecasting, usually the results get very close to the true or actual values simply because this model can be iteratively be adjusted so that errors are reduced. can Streamline Data Operations and enable The data was in structured format and was stores in a csv file. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. The authors Motlagh et al. (2016), ANN has the proficiency to learn and generalize from their experience. All Rights Reserved. ). Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. Figure 1: Sample of Health Insurance Dataset. Also with the characteristics we have to identify if the person will make a health insurance claim. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. 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 prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). 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. 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. 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. (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. Early health insurance amount prediction can help in better contemplation of the amount needed. "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). In I. history Version 2 of 2. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. So, without any further ado lets dive in to part I ! Insurance companies are extremely interested in the prediction of the future. You signed in with another tab or window. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. According to Rizal et al. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. 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. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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. According to Rizal et al. Numerical data along with categorical data can be handled by decision tress. Logs. In health insurance many factors such as pre-existing body condition, family medical history, Body Mass Index (BMI), marital status, location, past insurances etc affects the amount. The effect of various independent variables on the premium amount was also checked. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. II. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Implementing a Kubernetes Strategy in Your Organization? 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. Accuracy defines the degree of correctness of the predicted value of the insurance amount. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. Comments (7) Run. Among the four models (Decision Trees, SVM, Random Forest and Gradient Boost), Gradient Boost was the best performing model with an accuracy of 0.79 and was selected as the model of choice. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. You signed in with another tab or window. Approach : Pre . This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Abhigna et al. Introduction to Digital Platform Strategy? 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. Predicting the cost of claims in an insurance company is a real-life problem that needs to be , A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. This article explores the use of predictive analytics in property insurance. 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. (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. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Your email address will not be published. Abhigna et al. 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. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Fig. Model performance was compared using k-fold cross validation. These decision nodes have two or more branches, each representing values for the attribute tested. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. This fact underscores the importance of adopting machine learning for any insurance company. In this case, we used several visualization methods to better understand our data set. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. These inconsistencies must be removed before doing any analysis on data. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Example, Sangwan et al. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. Using this approach, a best model was derived with an accuracy of 0.79. The model was used to predict the insurance amount which would be spent on their health. Early health insurance amount prediction can help in better contemplation of the amount. Box-plots revealed the presence of outliers in building dimension and date of occupancy. 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.
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