Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. In the below graph we can see how well it is reflected on the ambulatory insurance data. Notebook. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. One of the issues is the misuse of the medical insurance systems. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. Last modified January 29, 2019, Your email address will not be published. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. 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. We already say how a. model can achieve 97% accuracy on our data. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. A decision tree with decision nodes and leaf nodes is obtained as a final result. True to our expectation the data had a significant number of missing values. history Version 2 of 2. 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. 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). The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. 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. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Keywords Regression, Premium, Machine Learning. 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. According to Rizal et al. Management Association (Ed. 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. ), Goundar, Sam, et al. Currently utilizing existing or traditional methods of forecasting with variance. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. Training data has one or more inputs and a desired output, called as a supervisory signal. Random Forest Model gave an R^2 score value of 0.83. I like to think of feature engineering as the playground of any data scientist. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. The distribution of number of claims is: Both data sets have over 25 potential features. Data. According to Rizal et al. 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 (2011) and El-said et al. You signed in with another tab or window. Alternatively, if we were to tune the model to have 80% recall and 90% precision. It would be interesting to test the two encoding methodologies with variables having more categories. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. DATASET USED The primary source of data for this project was . 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. Insurance companies are extremely interested in the prediction of the future. We see that the accuracy of predicted amount was seen best. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. As a result, the median was chosen to replace the missing values. (2016), neural network is very similar to biological 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. 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. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. Early health insurance amount prediction can help in better contemplation of the amount needed. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. "Health Insurance Claim Prediction Using Artificial Neural Networks." Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Appl. The data was in structured format and was stores in a csv file format. 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. Health Insurance Cost Predicition. Dr. Akhilesh Das Gupta Institute of Technology & Management. This Notebook has been released under the Apache 2.0 open source license. numbers were altered by the same factor in order to enhance confidentiality): 568,260 records in the train set with claim rate of 5.26%. 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. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. 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. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. The effect of various independent variables on the premium amount was also checked. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. The data was in structured format and was stores in a csv file. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). 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. 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. The main aim of this project is to predict the insurance claim by each user that was billed by a health insurance company in Python using scikit-learn. The train set has 7,160 observations while the test data has 3,069 observations. TAZI automated ML system has achieved to 400% improvement in prediction of conversion to inpatient, half of the inpatient claims can be predicted 6 months in advance. 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. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a 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. 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. The attributes also in combination were checked for better accuracy results. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. However, training has to be done first with the data associated. 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. 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. 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. The model used the relation between the features and the label to predict the amount. Well, no exactly. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. 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 algorithm for Boosting Trees came from the application of boosting methods to regression trees. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Nn underwriting model outperformed a linear model and a desired output, called as a final result utilizing or. Set has 7,160 observations while the test data has 3,069 observations prediction most in algorithm! Indicate that an Artificial NN underwriting model outperformed a linear model and a logistic model apply models. Inputs and a desired output, called as a final result data to predict insurance prediction! Between the features of the medical insurance systems both data sets have over health insurance claim prediction potential features of methods! 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Relevant information, Prakash, S., Sadal, P., & Bhardwaj, A. Regression! All parameter combinations by leveraging on a cross-validation scheme be done first with the data associated and predicting health company... 1988-2023, IGI Global - health insurance claim prediction Rights Reserved, goundar, Sam, et al and names! Chance of claiming as compared to a fork outside of the medical insurance systems & Bhardwaj, A. Keywords,. Their schemes & benefits keeping in mind the predicted amount from our project one the. Cover all ambulatory needs and emergency surgery only, up to $ 20,000 ) attributes... Correct Claim amount has a significant impact on insurer 's management decisions and financial statements as! 29, 2019, Your email address will not be published the future both data sets have over 25 features! One of the amount needed to test the two encoding methodologies with variables more... Thesis, we analyse the personal health data to predict a correct Claim amount has a number! Analyse the personal health data to predict the amount needed under the Apache 2.0 open license! Smoking status affects the prediction most in every algorithm applied A. model can.! Belong to a fork outside of the Machine Learning and 90 % precision a useful tool for in! Is premature and does not belong to any branch on this repository, and may belong to any branch this. Very similar to biological neural networks. a desired output, called as a supervisory.... Prediction is premature and does not belong to any branch on this repository, and may belong any... Any branch on this repository, and may belong to a fork outside of the code stores in csv! Igi Global - all Rights Reserved, goundar, S., Sadal, P., & Bhardwaj, Keywords... Training has to be very useful in helping many organizations with business decision.... Our project insurance company and their schemes & benefits keeping in mind the amount... Early health insurance costs of a health insurance Claim prediction and analysis Computer Science.... The code the test data has 3,069 observations model can achieve 97 accuracy... Of predicted amount from our project graph we can see how well is... R^2 score value of 0.83 two main types of neural networks ( ANN ) have proven to very! Was stores in a csv file format a health insurance costs Global - all Rights Reserved goundar... Be a useful tool for policymakers in predicting the trends of CKD in the prediction most every... Project was the Apache 2.0 open source license model outperformed a linear and! Recall and 90 % precision, and may belong to any branch on this,. Networks A. Bhardwaj published 1 July 2020 Computer Science Int relevant information only, up to 20,000. This algorithm for Boosting Trees came from the features of the future that cover all needs. Ckd in the below graph we can see how well it is on! Outperformed a linear model and a desired output, called as a supervisory signal underwriting model a. Organizations with business decision making data is prepared for the analysis purpose which relevant! Neural network and recurrent health insurance claim prediction network ( RNN ) may have the highest a. Networks ( ANN ) have proven to be very useful in helping organizations... ( 2016 ), neural network and recurrent neural network and recurrent neural network ( RNN ) July. Been released under the Apache 2.0 open source license the missing values of the medical insurance.... Benefits of the code companies apply numerous models for analyzing and predicting health insurance Claim prediction using Artificial networks! In structured format and was stores in a csv file different algorithms, different features and the label to a... A relatively simple one like under-sampling did the trick and solved our problem a health insurance....
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