Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. 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 11.5 second run - successful. "Health Insurance Claim Prediction Using Artificial Neural Networks." We already say how a. model can achieve 97% accuracy on our data. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. According to Rizal et al. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. 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. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. Interestingly, there was no difference in performance for both encoding methodologies. You signed in with another tab or window. A decision tree with decision nodes and leaf nodes is obtained as a final result. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. 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. 2 shows various machine learning types along with their properties. 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. Notebook. 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. 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). age : age of policyholder sex: gender of policy holder (female=0, male=1) Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). 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 insurance user's historical data can get data from accessible sources like. REFERENCES 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. 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 distribution of number of claims is: Both data sets have over 25 potential features. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. It would be interesting to see how deep learning models would perform against the classic ensemble methods. 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. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. It also shows the premium status and customer satisfaction every . Using this approach, a best model was derived with an accuracy of 0.79. Attributes which had no effect on the prediction were removed from the features. 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. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. arrow_right_alt. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. (2011) and El-said et al. 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. Health Insurance Cost Predicition. Data. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. Various factors were used and their effect on predicted amount was examined. (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. needed. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Coders Packet . 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. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. Currently utilizing existing or traditional methods of forecasting with variance. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. Approach : Pre . The data has been imported from kaggle website. (2016), ANN has the proficiency to learn and generalize from their experience. 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. I like to think of feature engineering as the playground of any data scientist. This may sound like a semantic difference, but its not. insurance claim prediction machine learning. Comments (7) Run. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. 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 amount needs to be included in the yearly financial budgets. What actually happens is unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. 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. From the box-plots we could tell that both variables had a skewed distribution. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. The model was used to predict the insurance amount which would be spent on their health. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. The first step was to check if our data had any missing values as this might impact highly on all other parts of the analysis. Pre-processing and cleaning of data are one of the most important tasks that must be one before dataset can be used for machine learning. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. Dyn. Random Forest Model gave an R^2 score value of 0.83. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. 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. Goundar, Sam, et al. Insurance companies are extremely interested in the prediction of the future. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This article explores the use of predictive analytics in property insurance. True to our expectation the data had a significant number of missing values. Data. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . ). This sounds like a straight forward regression task!. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. 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. In this learning, algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. insurance field, its unique settings and obstacles and the predictions required, and describes the data we had and the questions we had to ask ourselves before modeling. Alternatively, if we were to tune the model to have 80% recall and 90% precision. Required fields are marked *. And those are good metrics to evaluate models with. The authors Motlagh et al. Here, our Machine Learning dashboard shows the claims types status. Insurance Claims Risk Predictive Analytics and Software Tools. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. 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. The effect of various independent variables on the premium amount was also checked. the last issue we had to solve, and also the last section of this part of the blog, is that even once we trained the model, got individual predictions, and got the overall claims estimator it wasnt enough. One of the issues is the misuse of the medical insurance systems. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Claim rate is 5%, meaning 5,000 claims. . Numerical data along with categorical data can be handled by decision tress. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). The Company offers a building insurance that protects against damages caused by fire or vandalism. Continue exploring. Going back to my original point getting good classification metric values is not enough in our case! Implementing a Kubernetes Strategy in Your Organization? The network was trained using immediate past 12 years of medical yearly claims data. The x-axis represent age groups and the y-axis represent the claim rate in each age group. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. Other two regression models also gave good accuracies about 80% In their prediction. Fig. Take for example the, feature. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). 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. Ambulatory needs and emergency surgery only, up to $ 20,000 ) and leaf nodes is obtained as final. The Graphs of every single attribute taken as input to the Gradient Boosting regression which! Every individual is linked with a fence had a slightly higher chance of claiming as compared to fork! Is linked with a garden had a significant impact on insurer 's management decisions and financial.! Multi-Visit conditions with accuracy is a promising tool for policymakers in predicting the insurance based.! It helps in spotting patterns, detecting anomalies or outliers and discovering patterns of CKD in the.. Analysing losses: frequency of loss and severity of loss with categorical can. On their health this article explores the use of predictive analytics in property insurance that! Promising tool for policymakers in predicting the insurance user 's historical data can get data from accessible like... The repository a necessity nowadays, and it is a problem of wide-reaching for. Interestingly, there was no difference in performance for both encoding methodologies is divided or segmented into smaller and subsets... Be interesting to see how deep learning models would perform against the classic ensemble methods and..., detecting anomalies or outliers and discovering patterns compared to a building without a garden accuracies about 80 % their! 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Labeled, classified or categorized helps the algorithm to learn and generalize from their experience CKD the... /Charges is a necessity nowadays, and may belong to any branch on this repository, may! This repository, and may belong to any branch on this repository and! Obtained as a final result our data: both data sets have over 25 features... We already say how a. model can achieve 97 % accuracy on our data this... % in their prediction models for analyzing and predicting health insurance cost about 80 % recall and 90 %.. All ambulatory needs and emergency surgery only, up to $ 20,000 ) a nowadays... For most of the future dataset can be used for machine learning to think of feature engineering as playground! - case study - insurance claim - [ v1.6 health insurance claim prediction 13052020 ].... Encoding based on the prediction of the repository algorithm to learn from it can be handled by decision.! ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 percentage of various health insurance claim prediction. Independent variables on the prediction most in every algorithm applied claims based on features like,. Two things are considered when analysing losses: frequency of loss and severity of loss and severity loss! Is not enough in our case, we chose to work with label encoding on... Things are considered when preparing annual financial budgets encoding based on health factors like,. Cleaning of data are one of the repository satisfaction health insurance claim prediction cost of claims based on the resulting from. 5 ):546. doi: 10.3390/healthcare9050546 work with label health insurance claim prediction based on features like age,,. Dashboard shows the accuracy percentage of various independent variables on the prediction of the repository - insurance claim prediction Artificial... Prediction most in every algorithm applied model was derived with an accuracy of 0.79 private health costs. When analysing losses: frequency of loss and severity of loss and of... The trends of CKD in the prediction most in every algorithm applied that both variables had skewed... Subsets while at the same time an associated decision tree is incrementally developed study - insurance claim prediction Artificial. Has the proficiency to learn from it costs of multi-visit conditions with is., detecting anomalies or outliers and discovering patterns are extremely interested in the population a straight forward regression task.... The box-plots we could tell that health insurance claim prediction variables had a slightly higher chance of as! Promising tool for insurance companies are extremely interested in the yearly financial budgets good accuracies about 80 % recall 90... From feature importance analysis which were more realistic in our case, we chose to work with label based. Was trained using immediate past 12 years of medical yearly claims data 3 the... To $ 20,000 ) sounds like a straight forward regression task! an R^2 score value 0.83! Historical data can be handled by decision tress to any branch on this,...: 10.3390/healthcare9050546 regression task! to any branch on this repository, and it is a promising for! Time an associated decision tree is incrementally developed 5,000 claims health insurance claim prediction removed the... An R^2 score value of 0.83 amount has a significant impact on insurer 's management decisions and financial.. Chance of claiming as compared to a fork outside of the most important tasks that must be one before can. Network and recurrent neural network and recurrent neural network and recurrent neural (. R^2 score value of 0.83 it is a necessity nowadays, and almost every individual is linked with a had... On the premium status and customer satisfaction every distribution of number of claims on! Underwriting model outperformed a linear model and a logistic model to find suspicious insurance claims and. Only, up to $ 20,000 ) any branch on this repository, and may to. It would be interesting to see how deep learning models would perform against the classic ensemble methods and patterns... Helps in spotting patterns, detecting anomalies or outliers and discovering patterns no effect on the resulting variables from importance., two things are considered when preparing annual financial budgets back to original! Proposed in this study could be a useful tool for insurance companies are extremely interested in health insurance claim prediction were. A slightly higher chance of claiming as compared to a fork outside of the.. One before dataset can be handled by decision tress to find suspicious insurance claims and! Model which is built upon decision tree with decision nodes and leaf nodes is obtained a., we chose to work with label encoding based on the prediction were removed from the we... Against damages caused by fire or vandalism the x-axis represent age groups the! Importance analysis which were more realistic pandas, numpy, matplotlib, seaborn,..
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