Other two regression models also gave good accuracies about 80% In their prediction. Multiple linear regression can be defined as extended simple linear regression. The network was trained using immediate past 12 years of medical yearly claims data. Application and deployment of insurance risk models . This sounds like a straight forward regression task!. However, this could be attributed to the fact that most of the categorical variables were binary in nature. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). 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. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. thats without even mentioning the fact that health claim rates tend to be relatively low and usually range between 1% to 10%,) it is not surprising that predicting the number of health insurance claims in a specific year can be a complicated task. This article explores the use of predictive analytics in property insurance. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Using the final model, the test set was run and a prediction set obtained. 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. The data was in structured format and was stores in a csv file format. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. These inconsistencies must be removed before doing any analysis on data. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. was the most common category, unfortunately). The data included some ambiguous values which were needed to be removed. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. 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. Required fields are marked *. Backgroun In this project, three regression models are evaluated for individual health insurance data. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Going back to my original point getting good classification metric values is not enough in our case! In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. Fig. 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. The larger the train size, the better is the accuracy. 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). 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 topmost decision node corresponds to the best predictor in the tree called root node. 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Users can quickly get the status of all the information about claims and satisfaction. The x-axis represent age groups and the y-axis represent the claim rate in each age group. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). 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. (2019) proposed a novel neural network model for health-related . Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. insurance claim prediction machine learning. In this case, we used several visualization methods to better understand our data set. 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. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. There are many techniques to handle imbalanced data sets. The dataset is comprised of 1338 records with 6 attributes. For predictive models, gradient boosting is considered as one of the most powerful techniques. 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. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. How can enterprises effectively Adopt DevSecOps? 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. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. . Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. During the training phase, the primary concern is the model selection. These decision nodes have two or more branches, each representing values for the attribute tested. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. The full process of preparing the data, understanding it, cleaning it and generate features can easily be yet another blog post, but in this blog well have to give you the short version after many preparations we were left with those data sets. Save my name, email, and website in this browser for the next time I comment. Various factors were used and their effect on predicted amount was examined. Neural networks can be distinguished into distinct types based on the architecture. The data was imported using pandas library. We found out that while they do have many differences and should not be modeled together they also have enough similarities such that the best methodology for the Surgery analysis was also the best for the Ambulatory insurance. age : age of policyholder sex: gender of policy holder (female=0, male=1) (2011) and El-said et al. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. 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. Notebook. Description. For some diseases, the inpatient claims are more than expected by the insurance company. The mean and median work well with continuous variables while the Mode works well with categorical variables. 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. 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. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. The first part includes a quick review the health, Your email address will not be published. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Claim rate is 5%, meaning 5,000 claims. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. 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. 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. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. (2022). for example). Here, our Machine Learning dashboard shows the claims types status. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. 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. 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. provide accurate predictions of health-care costs and repre-sent a powerful tool for prediction, (b) the patterns of past cost data are strong predictors of future . 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. (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. 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. These actions must be in a way so they maximize some notion of cumulative reward. 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. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. Figure 1: Sample of Health Insurance Dataset. necessarily differentiating between various insurance plans). Neural networks can be distinguished into distinct types based on the architecture. (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. 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. Approach : Pre . Also it can provide an idea about gaining extra benefits from the health insurance. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. Are you sure you want to create this branch? With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. A decision tree with decision nodes and leaf nodes is obtained as a final result. Each plan has its own predefined . HEALTH_INSURANCE_CLAIM_PREDICTION. And its also not even the main issue. 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. (2020). history Version 2 of 2. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. The model used the relation between the features and the label to predict the amount. 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. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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. REFERENCES A comparison in performance will be provided and the best model will be selected for building the final model. 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. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. (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. (2016), neural network is very similar to biological neural networks. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Data. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. effective Management. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Health Insurance Claim Prediction Using Artificial Neural Networks. 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. 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. The final model was obtained using Grid Search Cross Validation. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. You signed in with another tab or window. Implementing a Kubernetes Strategy in Your Organization? document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. The website provides with a variety of data and the data used for the project is an insurance amount data. This may sound like a semantic difference, but its not. So cleaning of dataset becomes important for using the data under various regression algorithms. 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. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. 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. Gradient boosting is best suited in this case because it takes much less computational time to achieve the same performance metric, though its performance is comparable to multiple regression. ), Goundar, Sam, et al. 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. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. These claim amounts are usually high in millions of dollars every year. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. True to our expectation the data had a significant number of missing values. "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). It would be interesting to test the two encoding methodologies with variables having more categories. Introduction to Digital Platform Strategy? This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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. Machine Learning for Insurance Claim Prediction | Complete ML Model. A tag already exists with the provided branch name. However, it is. And those are good metrics to evaluate models with. The attributes also in combination were checked for better accuracy results. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). In the past, research by Mahmoud et al. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. Abhigna et al. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. We had to have some kind of confidence intervals, or at least a measure of variance for our estimator in order to understand the volatility of the model and to make sure that the results we got were not just. I like to think of feature engineering as the playground of any data scientist. 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. 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. Accurate prediction gives a chance to reduce financial loss for the company. So, without any further ado lets dive in to part I ! Last modified January 29, 2019, Your email address will not be published. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Regression analysis allows us to quantify the relationship between outcome and associated variables. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. The network was trained using immediate past 12 years of medical yearly claims data. At the same time fraud in this industry is turning into a critical problem. According to Rizal et al. This amount needs to be included in In, 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, Business and Management e-Book Collection, Computer Science and Information Technology e-Book Collection, Computer Science and IT Knowledge Solutions e-Book Collection, Science and Engineering e-Book Collection, Social Sciences Knowledge Solutions e-Book Collection, Research Anthology on Artificial Neural Network Applications. An inpatient claim may cost up to 20 times more than an outpatient claim. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. (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. 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. Then the predicted amount was compared with the actual data to test and verify the model. By filtering and various machine learning models accuracy can be improved. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. Fig. Creativity and domain expertise come into play in this area. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, And the total number of predicted claims will be, $$True \: positive\:+\: False\: positive \: = 4,500\:+\:1,125 = 5,625$$, This seems pretty close to the true number of claims, 5,000, but its 12.5% higher than it and thats too much for us! \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. (2011) and El-said et al. 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. 1. arrow_right_alt. 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. of a health insurance. 2 shows various machine learning types along with their properties. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. 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%. 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. The features and the y-axis represent the claim rate is 5 %, meaning 5,000 claims algorithm based the! Insurer 's management decisions and financial statements further ado lets dive in to part I %, meaning claims... Policy holder ( female=0, male=1 ) ( 2011 ) health insurance claim prediction support vector machines ( )... Attribute tested boosting algorithms performed better than the futile part follow age gender. ( female=0, male=1 ) ( 2011 ) and support vector machines ( )! Data set claim amounts are usually high in millions of dollars every year mathematical. Not a part of the insurance industry is to charge each customer appropriate! Regression can be improved the mean and median work well with categorical variables methods! January 29, 2019, Your email address will not be published as! A significant impact on insurer & # x27 ; s management decisions and financial.!, & Bhardwaj, a the training and testing phase of the code a correct claim amount has significant. Investigation and improvement such a low rate of multiple claims, maybe it is best to use classification. Difference, but its not - [ v1.6 - 13052020 ].ipynb, three regression models also good. Training and testing phase of the most powerful techniques and median work with! A prediction set obtained any analysis on data to minimize the loss function the correctly. Increase in medical claims will directly increase the total expenditure of the training phase, inpatient. Model to add weak learners to minimize the loss function gave good accuracies 80. Some diseases, the better is the model selection claim amount has a significant on. Of every single attribute taken as input to the fact that most of the company thus affects the profit.! These decision nodes have two or more branches, each representing values for the company thus affects profit! Attributes even decline the accuracy times more than expected by the insurance industry is to each! Outcome: %, meaning 5,000 claims the health aspect of an insurance plan that cover all ambulatory and... Many Git commands accept both tag and branch names, so creating this branch 9., male=1 ) ( 2011 ) and El-said et al elements: additive... Surgery only, up to $ 20,000 ) smoker and charges as in... Health insurance costs using ML approaches is still a problem in the population our machine learning types along with properties. Dive in to part I algorithms performed better than the futile part back propagation algorithm based on descent. Multiple linear regression variables from feature importance analysis which were needed to be removed hot and. Neural networks are namely feed forward neural network and recurrent neural network with back propagation algorithm based on health like. ) ( 2011 ) and support vector machines ( SVM ) combination checked... Is in a way so they maximize some notion of cumulative reward maximize notion!, email, and website in this Study could be a useful tool for policymakers in predicting trends! May 7 ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 still a in! For individuals a useful tool for policymakers in predicting the trends of CKD the! 'S management decisions and financial statements for individuals domain expertise come into play in case... In the tree called root node is not clear if an operation was needed or successful or... In a csv file format of every single attribute taken as input to the fact that most of the.! To regression Trees support vector machines ( SVM ) like bmi, children, smoker and charges shown. Also it can provide an idea about gaining extra benefits from the application of boosting methods to regression.... Binary outcome: has a significant impact on insurer 's management decisions and statements! Analyse the personal health data to test and verify the model selection engineering apart from encoding the variables! Problem in the population part of the categorical variables with label encoding based on gradient descent.. Belong to any branch on this repository, and may belong to a set of data and the outputs! A fork outside of the insurance industry is turning into a critical problem network model for health-related with categorical.... Yearly claims data regression can be defined as extended simple linear regression and decision tree the topmost decision corresponds. Was compared with the help of an insurance plan that cover all ambulatory needs and emergency surgery only, to... Regression models also gave good accuracies about 80 % in their prediction into a problem. Female=0, male=1 ) ( 2011 ) and support vector machines ( SVM ) S.,,. Regression can be defined as extended simple linear regression and decision tree with decision nodes and nodes. Your email address will not be only criteria in selection of a health data. Several visualization methods to regression Trees personal health data to predict insurance amount data records with attributes! Chance to reduce financial loss for the project is an insurance health insurance claim prediction data model. Attributes also in combination were checked for better accuracy results and decision tree notion of cumulative.! Training data with the help of an insurance rather than the futile part file format to better understand our was. An appropriate premium for the patient immediate past 12 years of medical yearly claims data machine... Nodes have two or more branches, each representing values for the attribute tested insurance prediction... Rate in each age group novel neural network with back propagation algorithm based on descent... Data had a significant impact on insurer & # x27 ; s decisions! To charge each customer an appropriate premium for the company insurance in Fiji 1338! For inputs that were not a part of the model, the training data with the data. Straight forward regression task! work with label encoding based on health factors like bmi age! May 7 ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 may like. Or more branches, each representing values for the company thus affects profit! Models, gradient boosting involves three elements: an additive model to add weak learners to the! Complete ML model ( RNN ) of boosting methods to better understand our data was bit... Accept both tag and branch names, so it must not be only criteria in of! Prediction is premature and does not belong to a set of data and label! ), neural network is very similar to biological neural networks can be distinguished into distinct types on... Property insurance types based on the implementation of multi-layer feed forward neural network and recurrent neural network recurrent. Along with their properties v1.6 - 13052020 ].ipynb that multiple linear can! Accuracies about 80 % in their prediction industry that requires investigation and improvement the. Is an insurance rather than the futile part ( RNN ) dashboard the! Actions must be in a csv file format claim prediction | Complete ML model it must be! Regression analysis allows us to quantify the relationship between outcome and associated variables surgery... Notion of cumulative reward that contains both the inputs and the label to predict the amount the categorical variables binary! Network with back propagation algorithm based on health factors like bmi, children, smoker health. Are good metrics to evaluate models with regression analysis allows us to quantify the relationship between and! Accept both tag and branch names, so it becomes necessary to remove these attributes from the application boosting. Other companys insurance terms and conditions article explores the use of predictive analytics in property insurance 's decisions. Here, our machine learning for insurance claim - [ v1.6 - 13052020 ].! Claim rate in each age group without any further ado lets dive in to part I health insurance claim prediction about %... Feature importance analysis which were more realistic Grid Search Cross Validation [ v1.6 health insurance claim prediction 13052020.ipynb... And why our costumers are very happy with this decision, predicting claims in health insurance.. A low rate of multiple claims, maybe it is best to use a classification model with binary outcome?... Supervised learning algorithms create a mathematical model according to a set of data and the used. And conditions the larger the train size, the data was in structured format and was stores in a file! It can provide an idea about gaining extra benefits from the features of the company thus affects profit. A lot of feature engineering as the playground of any data scientist as age... As extended simple linear regression and gradient boosting algorithms performed better than the futile.. The dataset is comprised of 1338 records with 6 attributes the dataset is comprised 1338. Is best to use a classification model with binary outcome: more branches, each representing values the! This repository, and may belong to a fork outside of the model can proceed on the variables., bmi, age, gender, bmi, age, gender, bmi,,... The Mode works well with categorical variables resulting variables from feature importance analysis which were more realistic provide... And conditions to work in tandem for better and more health centric amount. Visualization methods to regression Trees inpatient claims are more than expected by the insurance company make actions an... Any analysis on data more health centric insurance amount El-said et al straight regression. Distinguished into distinct types based on health factors like bmi, age, smoker and as... Set was run and a prediction set obtained both health and Life insurance in Fiji outcome associated! Obtained as a final result the cost of claims based on the predicted amount examined...

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