(2019) proposed a novel neural network model for health-related . Description. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. for the project. Machine learning can be defined as the process of teaching a computer system which allows it to make accurate predictions after the data is fed. Application and deployment of insurance risk models . 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. Coders Packet . Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. As a result, the median was chosen to replace the missing values. Health Insurance Claim Prediction Using Artificial Neural Networks: 10.4018/IJSDA.2020070103: A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. 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. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. 2 shows various machine learning types along with their properties. i.e. Users can quickly get the status of all the information about claims and satisfaction. 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. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. Keywords Regression, Premium, Machine Learning. According to Rizal et al. 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. Are you sure you want to create this branch? Refresh the page, check. 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. License. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. It would be interesting to test the two encoding methodologies with variables having more categories. 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. Data. The larger the train size, the better is the accuracy. Also it can provide an idea about gaining extra benefits from the health insurance. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. Insurance Claim Prediction Problem Statement A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The size of the data used for training of data has a huge impact on the accuracy of data. According to Kitchens (2009), further research and investigation is warranted in this area. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. trend was observed for the surgery data). At the same time fraud in this industry is turning into a critical problem. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The network was trained using immediate past 12 years of medical yearly claims data. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. 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. Required fields are marked *. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. A matrix is used for the representation of training data. In the past, research by Mahmoud et al. Also it can provide an idea about gaining extra benefits from the health insurance. The data was imported using pandas library. The x-axis represent age groups and the y-axis represent the claim rate in each age group. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Alternatively, if we were to tune the model to have 80% recall and 90% precision. Abhigna et al. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. The diagnosis set is going to be expanded to include more diseases. 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. "Health Insurance Claim Prediction Using Artificial Neural Networks." Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. Backgroun In this project, three regression models are evaluated for individual health insurance data. (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. Later the accuracies of these models were compared. Using the final model, the test set was run and a prediction set obtained. The train set has 7,160 observations while the test data has 3,069 observations. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. Using this approach, a best model was derived with an accuracy of 0.79. 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. The dataset is comprised of 1338 records with 6 attributes. 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. of a health insurance. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. According to Zhang et al. The model used the relation between the features and the label to predict the amount. https://www.moneycrashers.com/factors-health-insurance-premium- costs/, https://en.wikipedia.org/wiki/Healthcare_in_India, https://www.kaggle.com/mirichoi0218/insurance, https://economictimes.indiatimes.com/wealth/insure/what-you-need-to- know-before-buying-health- insurance/articleshow/47983447.cms?from=mdr, https://statistics.laerd.com/spss-tutorials/multiple-regression-using- spss-statistics.php, https://www.zdnet.com/article/the-true-costs-and-roi-of-implementing-, https://www.saedsayad.com/decision_tree_reg.htm, http://www.statsoft.com/Textbook/Boosting-Trees-Regression- Classification. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. Dr. Akhilesh Das Gupta Institute of Technology & Management. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. for example). The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. True to our expectation the data had a significant number of missing values. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. As you probably understood if you got this far our goal is to predict the number of claims for a specific product in a specific year, based on historic data. 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. This amount needs to be included in the yearly financial budgets. needed. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. It also shows the premium status and customer satisfaction every . Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. 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. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. That predicts business claims are 50%, and users will also get customer satisfaction. The real-world data is noisy, incomplete and inconsistent. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Fig. A decision tree with decision nodes and leaf nodes is obtained as a final result. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. HEALTH_INSURANCE_CLAIM_PREDICTION. 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. The different products differ in their claim rates, their average claim amounts and their premiums. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. The attributes also in combination were checked for better accuracy results. effective Management. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. All Rights Reserved. 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. By filtering and various machine learning models accuracy can be improved. 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. The models can be applied to the data collected in coming years to predict the premium. (2022). Attributes which had no effect on the prediction were removed from the features. : 685,818 records is comprised of 1338 records with 6 attributes idea about gaining extra benefits the! 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