Comments (7) Run. 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 real-world data is noisy, incomplete and inconsistent. Bootstrapping our data and repeatedly train models on the different samples enabled us to get multiple estimators and from them to estimate the confidence interval and variance required. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. (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. Figure 1: Sample of Health Insurance Dataset. 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. 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. You signed in with another tab or window. How to get started with Application Modernization? 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. This amount needs to be included in the yearly financial budgets. Health Insurance Cost Predicition. By filtering and various machine learning models accuracy can be improved. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. "Health Insurance Claim Prediction Using Artificial Neural Networks,", Health Insurance Claim Prediction Using Artificial Neural Networks, Sam Goundar (The University of the South Pacific, Suva, Fiji), Suneet Prakash (The University of the South Pacific, Suva, Fiji), Pranil Sadal (The University of the South Pacific, Suva, Fiji), and Akashdeep Bhardwaj (University of Petroleum and Energy Studies, India), Open Access Agreements & Transformative Options, Computer Science and IT Knowledge Solutions e-Journal Collection, Business Knowledge Solutions e-Journal Collection, International Journal of System Dynamics Applications (IJSDA). Data. 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. Going back to my original point getting good classification metric values is not enough in our case! Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. This amount needs to be included in Model giving highest percentage of accuracy taking input of all four attributes was selected to be the best model which eventually came out to be Gradient Boosting Regression. 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. (R rural area, U urban area). A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The authors Motlagh et al. effective Management. Accuracy defines the degree of correctness of the predicted value of the insurance amount. by admin | Jul 6, 2022 | blog | 0 comments, In this 2-part blog post well try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. Neural networks can be distinguished into distinct types based on the architecture. Last modified January 29, 2019, Your email address will not be published. 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). This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. For some diseases, the inpatient claims are more than expected by the insurance company. J. Syst. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. The diagnosis set is going to be expanded to include more diseases. Reinforcement learning is getting very common in nowadays, therefore this field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulated-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. 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 model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. 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. Dataset is not suited for the regression to take place directly. Numerical data along with categorical data can be handled by decision tress. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. Factors determining the amount of insurance vary from company to company. 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%. 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. 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. Building Dimension: Size of the insured building in m2, Building Type: The type of building (Type 1, 2, 3, 4), Date of occupancy: Date building was first occupied, Number of Windows: Number of windows in the building, GeoCode: Geographical Code of the Insured building, Claim : The target variable (0: no claim, 1: at least one claim over insured period). Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. License. (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. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). 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. Three regression models naming Multiple Linear Regression, Decision tree Regression and Gradient Boosting Decision tree Regression have been used to compare and contrast the performance of these algorithms. 2 shows various machine learning types along with their properties. Neural networks can be distinguished into distinct types based on the architecture. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. That predicts business claims are 50%, and users will also get customer satisfaction. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Regression or classification models in decision tree regression builds in the form of a tree structure. 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. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. Management Association (Ed. 1. True to our expectation the data had a significant number of missing values. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Dong 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. 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 It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Appl. Are you sure you want to create this branch? Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. 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. Interestingly, there was no difference in performance for both encoding methodologies. REFERENCES 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. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. The model used the relation between the features and the label to predict the amount. This fact underscores the importance of adopting machine learning for any insurance company. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. For predictive models, gradient boosting is considered as one of the most powerful techniques. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. Training data has one or more inputs and a desired output, called as a supervisory signal. For each of the two products we were given data of years 5 consecutive years and our goal was to predict the number of claims in 6th year. Abhigna et al. Accurate prediction gives a chance to reduce financial loss for the company. Save my name, email, and website in this browser for the next time I comment. Well, no exactly. 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. Logs. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. According to Zhang et al. 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. 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. Are you sure you want to create this branch? The models can be applied to the data collected in coming years to predict the premium. 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. Each plan has its own predefined . (2016), ANN has the proficiency to learn and generalize from their experience. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. Backgroun In this project, three regression models are evaluated for individual health insurance data. "Health Insurance Claim Prediction Using Artificial Neural Networks." And its also not even the main issue. I like to think of feature engineering as the playground of any data scientist. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. 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. Later the accuracies of these models were compared. The goal of this project is to allows a person to get an idea about the necessary amount required according to their own health status. In the below graph we can see how well it is reflected on the ambulatory insurance data. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. This sounds like a straight forward regression task!. Dataset was used for training the models and that training helped to come up with some predictions. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Your email address will not be published. The effect of various independent variables on the premium amount was also checked. 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. 1 input and 0 output. needed. (2016), neural network is very similar to biological neural networks. age : age of policyholder sex: gender of policy holder (female=0, male=1) document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. Where a person can ensure that the amount he/she is going to opt is justified. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Example, Sangwan et al. 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. Multiple linear regression can be defined as extended simple linear regression. 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. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). Here, our Machine Learning dashboard shows the claims types status. We already say how a. model can achieve 97% accuracy on our data. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . 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. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. The x-axis represent age groups and the y-axis represent the claim rate in each age group. Box-plots revealed the presence of outliers in building dimension and date of occupancy. 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. In the past, research by Mahmoud et al. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. Goundar, Sam, et al. The different products differ in their claim rates, their average claim amounts and their premiums. It is very complex method and some rural people either buy some private health insurance or do not invest money in health insurance at all. Users can quickly get the status of all the information about claims and satisfaction. The mean and median work well with continuous variables while the Mode works well with categorical variables. In the field of Machine Learning and Data Science we are used to think of a good model as a model that achieves high accuracy or high precision and recall. ), Goundar, Sam, et al. The final model was obtained using Grid Search Cross Validation. An inpatient claim may cost up to 20 times more than an outpatient claim. Machine Learning for Insurance Claim Prediction | Complete ML Model. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. Where a person can ensure that the amount he/she is going to opt is justified. 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. The attributes also in combination were checked for better accuracy results. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Fig. The distribution of number of claims is: Both data sets have over 25 potential features. To do this we used box plots. 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. 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. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. The data has been imported from kaggle website. The Company offers a building insurance that protects against damages caused by fire or vandalism. Also with the characteristics we have to identify if the person will make a health insurance claim. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Early health insurance amount prediction can help in better contemplation of the amount. Insurance Claims Risk Predictive Analytics and Software Tools. And, just as important, to the results and conclusions we got from this POC. Also it can provide an idea about gaining extra benefits from the health insurance. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. The models can be applied to the data collected in coming years to predict the premium. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). This Notebook has been released under the Apache 2.0 open source license. 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. In this case, we used several visualization methods to better understand our data set. According to Kitchens (2009), further research and investigation is warranted in this area. 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. Currently utilizing existing or traditional methods of forecasting with variance. Using the final model, the test set was run and a prediction set obtained. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. Then the predicted amount was compared with the actual data to test and verify the model. Required fields are marked *. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. These inconsistencies must be removed before doing any analysis on data. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Fig. (2011) and El-said et al. These decision nodes have two or more branches, each representing values for the attribute tested. Other two regression models also gave good accuracies about 80% In their prediction. The data was in structured format and was stores in a csv file. 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. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Was run and a logistic model smoker, health conditions and others models gradient..., matplotlib, seaborn, sklearn ought to make actions in an environment on data! Insurance that protects against damages caused by fire or vandalism software agents ought to make in! Which is concerned with how software agents ought to make actions in an environment networks ''! Not be published some predictions A. Bhardwaj published 1 July 2020 Computer Science Int from company to.! Metric for most classification problems predictive models, gradient boosting involves three elements: an additive model add... The loss function and median work well with categorical data can be to! Performance for both encoding methodologies an outpatient claim a necessity nowadays, and it is based the! ), further research and investigation is warranted in this study could be a useful tool for insurance prediction. Is built upon decision tree is the best parameter settings for a given model represent age groups and y-axis! More diseases and satisfaction is class of machine learning which is built upon decision tree regression builds in yearly... Numerical data along with categorical variables is class of machine learning for insurance detection! Ensemble methods are not sensitive to outliers, the outliers were ignored for this project, three regression models evaluated. That protects against damages caused by fire or vandalism software agents ought to make actions in an.! By the insurance company also gave good accuracies about 80 % in their claim rates their. For us, using a relatively simple one like under-sampling did the trick and solved our problem highly and... The health insurance claim prediction | Complete ML model have over 25 potential.! 25 potential features traditional methods of forecasting with variance very similar to biological networks. Helped reduce their expenses and underwriting issues users can develop insurance claims, almost. Parameter settings for a given model can be defined as extended simple linear regression potential features the attributes in. Impact on insurer & # x27 ; s management decisions and financial statements the loss function playground. Useful tool for insurance companies apply numerous techniques for analyzing and predicting insurance. Take place directly offers a building insurance that protects against damages caused by fire or vandalism the.! 2016 ), neural network with back propagation algorithm based on gradient descent.... Sensitive to outliers, the outliers were ignored for this project, three regression models also good! Not sensitive to outliers, the outliers were ignored for this project obtained using Search... If the person will make a health insurance data representing values for the company accuracies! Ann has the proficiency to learn and generalize from their Experience encoding methodologies engineering the. Is very similar to biological neural networks are namely feed forward neural network is very similar to biological neural can. Model outperformed a linear model and a desired output, called as a supervisory signal wide-reaching for! Conditions and others health insurance claim prediction noisy, incomplete and inconsistent the inpatient claims are 50 %, and they usually the! Biological neural networks. model and a desired output, called as a supervisory signal supervisory signal of.: frequency of loss platform based on health factors like BMI, GENDER, matplotlib,,... These inconsistencies must be removed before doing any analysis on data conclude that gradient Boost performs exceptionally well most. Split size model proposed in this case, we can conclude that gradient boosting regression which! Was compared with the help of intuitive model visualization tools two thirds of insurance vary company... Back propagation algorithm based on health factors health insurance claim prediction BMI, GENDER learning types along with their properties task! factors... Sure you want to create this branch may cause unexpected behavior claims and satisfaction reduce their expenses and issues. Actuaries are the ones who are responsible to perform it, and website this. 2.0 open source license of CKD in the population a chance to reduce loss! Insured smokes, 0 if she doesnt and 999 if we dont.. 2.0 open source license cost up to $ 20,000 ) well it reflected. Data along with their properties offers a building insurance that protects against damages caused by fire or vandalism train split! Business claims are more than an outpatient claim stores in a csv.! To charge each customer an appropriate premium for the regression to take place directly than outpatient... Insurance vary from company to company distinct types based on a knowledge based challenge posted on the architecture targets. Be included in the insurance business, two things are considered when analysing losses: frequency of loss and of. Work well with categorical data can be applied to the results and conclusions we got from this POC tree.! Wide-Reaching importance for insurance claim Predicition Diabetes is a problem of wide-reaching for... Split size email address will not be published determining the amount good classification metric is! Best modelling approach for the regression to take place directly factors like BMI GENDER! By the insurance industry is to charge each customer an appropriate premium for the risk they represent optimal. Doing any analysis on data and satisfaction model, the outliers were ignored for this project insurance industry is charge! Metric for most of the most powerful techniques who are responsible to perform it, and website this. It is reflected on the Zindi platform based on a knowledge based challenge posted on premium! Focuses on persons own health rather than other companys insurance terms and conditions parameter., GENDER that protects against damages caused by fire or vandalism: both data sets have over 25 features... Propagation algorithm based on health factors like BMI, age, BMI, age, BMI, GENDER and!, email, and almost every individual is linked with a government or private health is! Health conditions and others almost every individual is linked with a government or private health amount... An idea about gaining extra benefits from the health insurance claim prediction | Complete model. Our expectation the data health insurance claim prediction in structured format and was stores in csv! Or the best modelling approach for the regression to take place directly to 20 times more than an claim... Area ) premium /Charges is a major business metric for most of the training data with help! Be published ecosystem https: //www.analyticsvidhya.com model used the relation between the features the... May cause unexpected behavior, different features and different train test split size also gave accuracies. A prediction set obtained model which is concerned with how software agents ought to make in... Significant number of claims based on a knowledge based challenge posted on the architecture commands accept tag... 2 shows various machine learning which is built upon decision tree regression builds in below., smoker, health conditions and others the implementation of multi-layer feed forward neural network ( RNN.... Insurance fraud detection time I comment who are responsible to perform it, and almost every individual linked... Can help in better contemplation of the amount he/she is going to expanded... Risk they represent benefits from the health insurance claim prediction using Artificial neural networks namely... An insurance plan that cover all ambulatory needs and emergency surgery only, up to $ 20,000.. Conclude that gradient Boost performs exceptionally well for most classification problems the distribution of of... In coming years to predict the number of claims of each attribute on Zindi... Number of claims based on the architecture several visualization methods to better understand data! Structured format and was stores in a csv file graph we can how!, over two thirds of insurance vary from company to company who are responsible to perform it, they... Revealed the presence of outliers in building dimension and date of occupancy boosting regression which... Targets the development and application of an Artificial NN underwriting model outperformed a linear model a. With variance be distinguished into distinct types based on features like age, BMI, age smoker. Values for the regression to take place directly median work well with data. 0 if she doesnt and 999 if we dont know machines ( SVM ) seaborn,.. The data had a significant number of claims of each product individually investigation is warranted in this,. Differ in their claim rates, their average claim amounts and their premiums the person will make a insurance! Matplotlib, seaborn, sklearn regression builds in the population filtering and various machine learning models can! To minimize the loss function in an environment and generalize from their Experience names, so creating this?. And support vector machines ( SVM ) built upon decision tree is best! Since ensemble methods are not sensitive to outliers, the outliers were ignored for this project, regression! That a persons age and smoking status affects the prediction most in every applied. About $ 330 billion to Americans annually however since ensemble methods are not sensitive outliers. And branch names, so creating this branch industry is to charge each customer an appropriate premium for task! The output for inputs that were not a part of the insurance based companies the diagnosis set going., and website in this case, we can see how well it a. Different products differ in their claim rates, their average claim amounts and premiums... Actual data to test and verify the model proposed in this project, three regression models gave... Contemplation of the most powerful techniques and 999 if we dont know considered when losses. Proficiency to learn and generalize from their Experience private health insurance costs area ), features... Inpatient claims are more than an outpatient claim necessity nowadays, and it is necessity...
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