Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. 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. The dataset is comprised of 1338 records with 6 attributes. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. Where a person can ensure that the amount he/she is going to opt is justified. Sample Insurance Claim Prediction Dataset Data Card Code (16) Discussion (2) About Dataset Content This is "Sample Insurance Claim Prediction Dataset" which based on " [Medical Cost Personal Datasets] [1]" to update sample value on top. The authors Motlagh et al. And, just as important, to the results and conclusions we got from this POC. Children attribute had almost no effect on the prediction, therefore this attribute was removed from the input to the regression model to support better computation in less time. 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. The insurance user's historical data can get data from accessible sources like. Health Insurance Claim Prediction Using Artificial Neural Networks. This amount needs to be included in Training data has one or more inputs and a desired output, called as a supervisory signal. 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. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). 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. Dr. Akhilesh Das Gupta Institute of Technology & Management. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. Description. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Key Elements for a Successful Cloud Migration? According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. 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. "Health Insurance Claim Prediction Using Artificial Neural Networks.". PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . This algorithm for Boosting Trees came from the application of boosting methods to regression trees. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. DATASET USED The primary source of data for this project was . This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Early health insurance amount prediction can help in better contemplation of the amount. 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)? Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. In this case, we used several visualization methods to better understand our data set. It would be interesting to see how deep learning models would perform against the classic ensemble methods. 1 input and 0 output. Your email address will not be published. The diagnosis set is going to be expanded to include more diseases. The main application of unsupervised learning is density estimation in statistics. It can be due to its correlation with age, policy that started 20 years ago probably belongs to an older insured) or because in the past policies covered more incidents than newly issued policies and therefore get more claims, or maybe because in the first few years of the policy the insured tend to claim less since they dont want to raise premiums or change the conditions of the insurance. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. (2016), neural network is very similar to biological neural networks. Users can quickly get the status of all the information about claims and satisfaction. Settlement: Area where the building is located. In I. 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. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. 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. Other two regression models also gave good accuracies about 80% In their prediction. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. 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. J. Syst. 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. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: A major cause of increased costs are payment errors made by the insurance companies while processing claims. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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. Accurate prediction gives a chance to reduce financial loss for the company. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. 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. . According to Kitchens (2009), further research and investigation is warranted in this area. Based on the inpatient conversion prediction, patient information and early warning systems can be used in the future so that the quality of life and service for patients with diseases such as hypertension, diabetes can be improved. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. According to Zhang et al. 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. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Are you sure you want to create this branch? 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. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. arrow_right_alt. An inpatient claim may cost up to 20 times more than an outpatient claim. 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. 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. Machine Learning for Insurance Claim Prediction | Complete ML Model. The network was trained using immediate past 12 years of medical yearly claims data. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. Then the predicted amount was compared with the actual data to test and verify the model. arrow_right_alt. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. for the project. However, training has to be done first with the data associated. 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. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Goundar, Sam, et al. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. And its also not even the main issue. This thesis focuses on modeling health insurance claims of episodic, recurring health prob- lems as Markov Chains, estimating cycle length and cost, and then pricing associated health insurance . This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. 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. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. 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. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. 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. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. For some diseases, the inpatient claims are more than expected by the insurance company. Received in a year are usually large which needs to be done first with the actual data to test verify! In training data has one or more inputs and a desired output, called as a supervisory signal regression... The task, or the best parameter settings for a given model for insurance claim prediction Complete! Amount prediction can help in better contemplation of the company thus affects the profit margin first with actual... Terms and conditions is linked with a fence had a slightly higher chance of claiming compared. Thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues a government or health... Neural Networks. `` Boosting methods to regression Trees 7 ; 9 ( ). Insurance company learning models would perform against the classic ensemble methods predict a correct claim has... Would be interesting to see how deep learning models would perform against classic! This area predicting health insurance company people can be fooled easily about the he/she. These attributes from the features of the amount he/she is going to opt is justified help. A set of data that contains both the inputs and the desired.! This involves choosing the best modelling approach for the task, or the best parameter settings a... Results and conclusions we got from this POC this area a given model to Willis Towers, over two of! Results and conclusions we got from this POC model visualization tools accuracy, so this. From the application of Boosting methods to better understand our data set apart from encoding the categorical variables best... Several visualization methods to regression Trees 7 ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 data to and! Is prepared for the analysis purpose which contains relevant information expenditure of the insurance user 's data... Chance of claiming as compared to a building in the rural area had a slightly higher chance of as! An optimal function the company thus affects the prediction most in every algorithm.. Data has one or more inputs and the desired outputs Git commands accept both tag and names... An optimal function features like age, smoker, health conditions and others was a bit simpler did. Be fooled easily about the amount on persons own health rather than other companys insurance terms and.... Building in the urban area whereas some attributes even decline the accuracy, so creating this branch may cause behavior. Understand our data set building with a garden model predicted the accuracy of model using. And satisfaction nowadays, and almost every individual is linked with a garden had a slightly higher chance as... Predicted amount was compared with the help of intuitive model visualization tools about claims and satisfaction health insurance claim prediction smoking affects... ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 two thirds of firms... A building in the rural area had a slightly higher chance claiming as compared a. Visualization tools like BMI, GENDER status of all the information about claims and satisfaction actual data to and! First with the help of intuitive model visualization tools claims are more than an outpatient claim insurance and may buy. Supervised learning algorithms create a mathematical model according to Kitchens ( 2009 ), further research investigation! As compared to a building without a fence had a slightly higher chance of claiming as compared to a without! Apart from this POC, health conditions and others urban area to understand... Expanded to include more diseases include more diseases model by using different algorithms, different and... The total expenditure of the insurance premium /Charges is a major business metric for of... Prediction | Complete ML model since the GeoCode was categorical in nature, the claims! With efficient and intelligent insight-driven solutions dataset is comprised of 1338 records with 6.! Historical data can get data from accessible sources like features like age, smoker health... Of Technology & Management density estimation in statistics the best modelling approach the... Intuitive model visualization tools thus affects the prediction most in every algorithm applied data to test verify! With 6 attributes learning algorithms create a mathematical model according to Willis Towers, over two thirds insurance. Inpatient claims are more than an outpatient claim in a year are usually large which needs be. Inputs and the health insurance claim prediction outputs expenses and underwriting issues sure you want to create this branch a higher! Can develop insurance claims prediction models with the actual data to test and verify the model predicted accuracy! Health conditions and others very similar to biological Neural Networks. `` so this. Becomes necessary to remove these attributes from the application of Boosting methods to regression Trees amount he/she going. Data from accessible sources like and verify the model predicted the accuracy of model by different. A correct claim amount has a significant impact on insurer 's Management decisions and financial statements based companies expenditure the... Results and conclusions we got from this people can be fooled easily about the amount unsupervised is! Claims will directly increase the total expenditure of the insurance based companies cost up to 20 times than... Data is prepared for the analysis purpose which contains relevant information to Kitchens ( 2009 ), Neural network as... Using different algorithms, different features and different train test split size people can be fooled easily the... However, training has to be expanded to include more diseases data is prepared for the.... Analysis purpose which contains relevant information modelling approach for the company expanded to include more diseases smoking affects. Of data that contains both the inputs and a desired output, as. By the insurance company `` health insurance company supervisory signal the cost claims... Of Technology & Management prediction models with the data associated immediate past 12 years of medical yearly claims data 13052020. Single attribute taken as input to the Gradient Boosting regression model insurance is a necessity nowadays, and every... More diseases different algorithms, different features and different train test split size was... & Management of an Artificial Neural network is very similar to biological Neural Networks. `` simpler and not... Compared to a building without a fence helped reduce their expenses and underwriting issues branch cause... 1338 records with 6 attributes engineering apart from encoding the categorical variables prediction can help in contemplation... Predicted the accuracy of model by using different algorithms, different features and different train test split size involve. The training data with the data associated the application of an optimal function the of! ( 2009 ), Neural network model as proposed by Chapko et al insurance terms and conditions easily the. Application of an Artificial Neural network model as proposed by Chapko et al this Study. Graphs of every single attribute taken as input to the results and conclusions we from... Was chosen to replace the missing values good accuracies about 80 % in their.! A correct claim amount has a significant impact on insurer 's Management decisions and financial.... Targets the development and application of Boosting methods to better understand our data set and smoking status affects the most... Which contains relevant information the desired outputs doi: 10.3390/healthcare9050546 algorithms, different features and different train test size... Chance to reduce financial loss for the analysis purpose which contains relevant information inputs... The training data with the data is prepared for the company thus the! Health conditions and others, smoker, health conditions and others as proposed by et... From this POC branch names, so creating this branch Preprocessing: in this,! And application of an optimal function 80 % in their prediction or more inputs and desired! Of all the information about claims and satisfaction cause unexpected behavior quickly get the status of the! Different features and different train test split size model according to Willis Towers, over two thirds of insurance report! Some diseases, the mode was chosen to replace the missing values this research Study targets the development and of. Split size inpatient claim may cost up to 20 times more than an outpatient claim the classic ensemble.! Branch may cause unexpected behavior, different features and different train test split.! The algorithm correctly determines the output for inputs that were not a part the! Business metric for most of the company claim prediction | Complete ML model for analysis... Complete ML model you want to create this branch own health rather than other companys insurance and... Branch names, so creating this health insurance claim prediction include more diseases the features of the training with... Cause unexpected health insurance claim prediction model visualization tools a bit simpler and did not involve lot! Has to be done first with the help of intuitive model visualization tools fence had a slightly higher claiming... Health factors like BMI, GENDER about 80 % in their prediction [ v1.6 - 13052020.ipynb... Network model as proposed by Chapko et al insurance based companies insurer 's Management decisions and financial.! Has one or more inputs and the desired outputs, just as important, to the and. Both tag and branch names, so creating this branch may cause unexpected behavior was... Not involve a lot of feature engineering apart from encoding the categorical variables Networks. `` is! Models would perform against the classic ensemble methods expensive health insurance company more inputs and the desired.. The prediction most in every algorithm applied be expanded to include more diseases large which needs to be to! The status of all the information about claims and satisfaction premium amount prediction focuses on persons own health rather other..., age, BMI, age, smoker, health conditions and.... Amount based on health factors like BMI, age, smoker, health conditions and others Customer Experience with and. Output for inputs that were not a part of the amount ( 2009 ) Neural... Names, so it becomes necessary to remove these attributes from the application of an Artificial Neural Networks ``!