It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. 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. 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. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. 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. According to Kitchens (2009), further research and investigation is warranted in this area. 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. In a dataset not every attribute has an impact on the prediction. 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. A major cause of increased costs are payment errors made by the insurance companies while processing claims. 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. According to Rizal et al. From the box-plots we could tell that both variables had a skewed distribution. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. Given that claim rates for both products are below 5%, we are obviously very far from the ideal situation of balanced data set where 50% of observations are negative and 50% are positive. Are you sure you want to create this branch? The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. Removing such attributes not only help in improving accuracy but also the overall performance and speed. Required fields are marked *. 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. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. Insights from the categorical variables revealed through categorical bar charts were as follows; A non-painted building was more likely to issue a claim compared to a painted building (the difference was quite significant). The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. These claim amounts are usually high in millions of dollars every year. Insurance companies are extremely interested in the prediction of the future. DATASET USED The primary source of data for this project was . 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. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. Logs. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. 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. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. . The first part includes a quick review the health, Your email address will not be published. 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. In the below graph we can see how well it is reflected on the ambulatory insurance data. The data was imported using pandas library. (2022). CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. You signed in with another tab or window. Accurate prediction gives a chance to reduce financial loss for the company. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. Random Forest Model gave an R^2 score value of 0.83. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Training data has one or more inputs and a desired output, called as a supervisory signal. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Those setting fit a Poisson regression problem. 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. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The authors Motlagh et al. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. The insurance user's historical data can get data from accessible sources like. 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. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. A tag already exists with the provided branch name. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. history Version 2 of 2. So, without any further ado lets dive in to part I ! 99.5% in gradient boosting decision tree regression. Health Insurance Claim Prediction Using Artificial Neural Networks. Figure 1: Sample of Health Insurance Dataset. ), 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. 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. (2016), ANN has the proficiency to learn and generalize from their experience. Dong et al. Health Insurance Cost Predicition. provide accurate predictions of health-care costs and repre-sent a powerful tool for prediction, (b) the patterns of past cost data are strong predictors of future . 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. Later the accuracies of these models were compared. 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. 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? Claim rate is 5%, meaning 5,000 claims. 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. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. The basic idea behind this is to compute a sequence of simple trees, where each successive tree is built for the prediction residuals of the preceding tree. age : age of policyholder sex: gender of policy holder (female=0, male=1) The Company offers a building insurance that protects against damages caused by fire or vandalism. 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. Currently utilizing existing or traditional methods of forecasting with variance. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. 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 . Insurance companies apply numerous techniques for analysing and predicting health insurance costs. The data included some ambiguous values which were needed to be removed. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. 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. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. II. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. You signed in with another tab or window. 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. 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. 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