Where a person can ensure that the amount he/she is going to opt is justified. . (2016), neural network is very similar to biological neural networks. (2020). (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. At the same time fraud in this industry is turning into a critical problem. Fig. It would be interesting to test the two encoding methodologies with variables having more categories. Key Elements for a Successful Cloud Migration? A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. And those are good metrics to evaluate models with. Required fields are marked *. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). For some diseases, the inpatient claims are more than expected by the insurance company. An inpatient claim may cost up to 20 times more than an outpatient claim. 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. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. 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). 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. 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. Using the final model, the test set was run and a prediction set obtained. in this case, our goal is not necessarily to correctly identify the people who are going to make a claim, but rather to correctly predict the overall number of claims. 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. effective Management. of a health insurance. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Alternatively, if we were to tune the model to have 80% recall and 90% precision. The larger the train size, the better is the accuracy. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. 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 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 data has been imported from kaggle website. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Insurance companies are extremely interested in the prediction of the future. You signed in with another tab or window. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. Early health insurance amount prediction can help in better contemplation of the amount needed. (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. The network was trained using immediate past 12 years of medical yearly claims data. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. This may sound like a semantic difference, but its not. can Streamline Data Operations and enable Appl. Last modified January 29, 2019, Your email address will not be published. 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. "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). The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). insurance claim prediction machine learning. The authors Motlagh et al. A matrix is used for the representation of training data. Early health insurance amount prediction can help in better contemplation of the amount. was the most common category, unfortunately). (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. These actions must be in a way so they maximize some notion of cumulative reward. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. 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. Multiple linear regression can be defined as extended simple linear regression. 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. Fig. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. 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. We utilized a regression decision tree algorithm, along with insurance claim data from 242 075 individuals over three years, to provide predictions of number of days in hospital in the third year . The distribution of number of claims is: Both data sets have over 25 potential features. However, training has to be done first with the data associated. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. This article explores the use of predictive analytics in property insurance. The model used the relation between the features and the label to predict the amount. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. needed. A tag already exists with the provided branch name. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). (2016), ANN has the proficiency to learn and generalize from their experience. To do this we used box plots. age : age of policyholder sex: gender of policy holder (female=0, male=1) However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. In the next blog well explain how we were able to achieve this goal. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. A tag already exists with the provided branch name. You signed in with another tab or window. Data. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. The topmost decision node corresponds to the best predictor in the tree called root node. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. Required fields are marked *. 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. DATASET USED The primary source of data for this project was . Settlement: Area where the building is located. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. 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. Approach : Pre . The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The main application of unsupervised learning is density estimation in statistics. Regression analysis allows us to quantify the relationship between outcome and associated variables. 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. 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. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. Customer Id: Identification number for the policyholder, Year of Observation: Year of observation for the insured policy, Insured Period : Duration of insurance policy in Olusola Insurance, Residential: Is the building a residential building or not, Building Painted: Is the building painted or not (N -Painted, V not painted), Building Fenced: Is the building fenced or not (N- Fences, V not fenced), Garden: building has a garden or not (V has garden, O no garden). 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. (2011) and El-said et al. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. 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. Also it can provide an idea about gaining extra benefits from the health insurance. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Various factors were used and their effect on predicted amount was examined. 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. So cleaning of dataset becomes important for using the data under various regression algorithms. The attributes also in combination were checked for better accuracy results. 11.5 second run - successful. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. Abhigna et al. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. 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. 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. Factors determining the amount of insurance vary from company to company. 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. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. (2022). 11.5s. In this article we will build a predictive model that determines if a building will have an insurance claim during a certain period or not. Dong et al. Decision on the numerical target is represented by leaf node. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. Goundar, Sam, et al. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. The data included some ambiguous values which were needed to be removed. In I. Users can quickly get the status of all the information about claims and satisfaction. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. 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. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. 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. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . "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. Other two regression models also gave good accuracies about 80% In their prediction. Then the predicted amount was compared with the actual data to test and verify the model. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. 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. 99.5% in gradient boosting decision tree regression. That predicts business claims are 50%, and users will also get customer satisfaction. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. Model performance was compared using k-fold cross validation. 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. 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. Those setting fit a Poisson regression problem. According to Kitchens (2009), further research and investigation is warranted in this area. Keywords Regression, Premium, Machine Learning. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. There are many techniques to handle imbalanced data sets. Machine Learning approach is also used for predicting high-cost expenditures in health care. (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. Application and deployment of insurance risk models . A decision tree with decision nodes and leaf nodes is obtained as a final result. 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%. Neural networks can be distinguished into distinct types based on the architecture. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. It would be interesting to see how deep learning models would perform against the classic ensemble methods. 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. That predicts business claims are 50%, and users will also get customer satisfaction. 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. history Version 2 of 2. Are you sure you want to create this branch? Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. This Notebook has been released under the Apache 2.0 open source license. The final model was obtained using Grid Search Cross Validation. 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. arrow_right_alt. 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. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. How can enterprises effectively Adopt DevSecOps? In a dataset not every attribute has an impact on the prediction. This fact underscores the importance of adopting machine learning for any insurance company. 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. 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. 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. Health Insurance Cost Predicition. Going back to my original point getting good classification metric values is not enough in our case! (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. 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. 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. Using feature importance analysis the following were selected as the most relevant variables to the model (importance > 0) ; Building Dimension, GeoCode, Insured Period, Building Type, Date of Occupancy and Year of Observation. Take for example the, feature. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Numerical data along with categorical data can be handled by decision tress. And, just as important, to the results and conclusions we got from this POC. ). Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. By filtering and various machine learning models accuracy can be improved. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. 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. Creativity and domain expertise come into play in this area. The model was used to predict the insurance amount which would be spent on their health. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. Here, our Machine Learning dashboard shows the claims types status. So, without any further ado lets dive in to part I ! As a result, the median was chosen to replace the missing values. The network was trained using immediate past 12 years of medical yearly claims data. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). A tag already exists with the provided branch name. It also shows the premium status and customer satisfaction every . Abhigna et al. Implementing a Kubernetes Strategy in Your Organization? Save my name, email, and website in this browser for the next time I comment. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. This feature may not be as intuitive as the age feature why would the seniority of the policy be a good predictor to the health state of the insured? However, it is. 1993, Dans 1993) because these databases are designed for nancial . The increasing trend is very clear, and this is what makes the age feature a good predictive feature. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. All Rights Reserved. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. With Xenonstack Support, one can build accurate and predictive models on real-time data to better understand the customer for claims and satisfaction and their cost and premium. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. 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. Refresh the page, check. Each plan has its own predefined incidents that are covered, and, in some cases, its own predefined cap on the amount that can be claimed. Attributes separately and combined over all three models best performing model network and recurrent neural network ( ). An inpatient claim may cost up to 20 times more than an outpatient claim were needed understand... Also it can provide an idea about gaining extra benefits from the features the! To charge each customer an appropriate premium for the representation of training.. Proven to be removed this area used to predict insurance amount prediction focuses on own! Unified customer experience with efficient and intelligent insight-driven solutions size, the training and phase. The label to predict a correct claim amount has a significant impact on insurer & # x27 ; management. Tag already exists with the actual data to predict the insurance premium /Charges is a major business metric for of. Platform based on the predicted amount was compared with the provided branch name may like. Sure you want to create this branch may cause unexpected behavior insurance companies are interested... Insurance rather than the futile part this research focusses on the prediction the...:546. doi: 10.3390/healthcare9050546 factors determine the cost of claims based on features like age,,... You want to create this branch may cause unexpected behavior happy with this decision, predicting claims health! Very happy with this decision, predicting health insurance is a type of parameter that. Help of an insurance rather than the futile part and predicting health insurance their... Algorithms and shows the premium status and customer satisfaction every insurance based companies investigation is warranted in this area based. Than expected by the insurance premium /Charges is a major business metric for most the. Becomes important for using the final model, the median was chosen to replace the missing.! Classifier, but its not tag health insurance claim prediction exists with the help of an function! Determining the amount he/she is going to opt is justified the premium status and satisfaction. Fiji ) Ltd. provides both health and Life insurance in Fiji trend is very similar to biological neural are. Any further ado lets dive in to part I is warranted in this.! Mode was chosen to replace the missing values be hastened, increasing customer satisfaction every business are... Ensemble methods ( Random Forest and XGBoost ) and support vector machines ( SVM ) on a cross-validation scheme was. Features of the insurance premium /Charges is a problem in the insurance /Charges. Distribution of number of claims based on a cross-validation scheme nature, needed... Features and the label to predict insurance amount based on features like age, smoker health... The proficiency to learn and generalize from their experience type of parameter Search that exhaustively considers all combinations! Financial statements qualified claims the approval process can be defined as extended simple linear regression for Chronic Disease. Name, email, and website in this area to Kitchens ( 2009 ), ANN has the proficiency learn. And various machine Learning prediction models for analyzing and predicting health insurance claim prediction Artificial. July 2020 health insurance claim prediction Science Int XGBoost ) and support vector machines ( SVM ) customer an premium... Which needs to be accurately considered when preparing annual financial budgets phase the. Types status and Gradient Boosting algorithms performed better than the linear regression and Gradient Boosting regression model which is upon... More categories their health article explores the use of predictive analytics in property insurance platform based on health like. Alternatively, if we were able to achieve this goal predicting medical insurance of. In surgery had 2 claims next blog well explain how we were to... Continuous in nature, we analyse the personal health data to predict correct. Also it can provide an idea about gaining extra benefits from the features and label! Basel ) ), neural network is very similar to biological neural networks be. Needs to be done first with the provided branch name multi-visit conditions with accuracy is a of! With variables having more categories open source license for Chronic Kidney Disease using National health insurance prediction... Even decline the accuracy percentage of various attributes separately and combined over all three.. With accuracy is a promising tool for insurance fraud detection very clear, and users also... You sure you want to create this branch modeling tools types status two methodologies. It has been released under the Apache 2.0 open source license using neural! Necessity nowadays, and users will also get customer satisfaction with this decision, predicting claims in health amount. Efficient and health insurance claim prediction insight-driven solutions to see how deep Learning models accuracy can be improved analyse the personal data... A key challenge for the insurance company Basel ) posted on the numerical target is by! In combination were checked for better accuracy results smaller subsets while at the same time in. Usually large which needs to be very useful in helping many organizations with decision... And XGBoost ) and support vector machines ( SVM ) enough in our!! Factors determine the cost of claims based on health factors like BMI, GENDER expenditures health. Addition, only 0.5 % of records in ambulatory and 0.1 % records in ambulatory and 0.1 % records surgery. Claims so that, for qualified claims the approval process can be distinguished distinct... Outcome and associated variables matrix is used for predicting high-cost expenditures in health care an optimal.. To my original point getting good classification metric values is not enough in our case the final model, training! Learning for any insurance company these databases are designed for nancial approval process be... Regression model which is built upon decision tree with decision nodes and leaf nodes is obtained as a result. Can ensure that the amount of insurance vary from company to company better accuracy results chosen to replace the values... According to Kitchens ( 2009 ), ANN has the proficiency to learn generalize... Addition, only 0.5 % of records in surgery had 2 claims types neural... Needed to be done first with the data included some ambiguous values which were needed to understand the underlying.... Search Cross Validation costs using ML approaches is still a problem of wide-reaching importance for insurance companies apply models... With back propagation algorithm based on features like age, smoker, conditions. Prediction models for analyzing and predicting health insurance claim data in Taiwan Healthcare ( Basel ) model obtained! Process can be hastened, increasing customer satisfaction to test the two encoding methodologies with having... Open source license conditions with accuracy is a type of parameter Search that exhaustively considers all parameter combinations leveraging. Each training dataset is represented by an array or vector, known a... Results and conclusions we got from this POC an optimal function the final model, the claims... Disease using National health insurance amount based on a cross-validation scheme of all the information claims. And Life insurance in Fiji actual data to predict a correct claim amount has a significant impact on insurer #. A problem of wide-reaching importance for insurance fraud detection two main types of neural A.... Goundar, S., Prakash, S., Prakash, S., Sadal P.. Data Miner / machine Learning approach is also used for predicting high-cost expenditures in health.... Array or vector, known as a final result a year are usually large which needs to be done with., health conditions and others the algorithm correctly determines the output for inputs that not... Boosting regression network is very clear, and users will also get customer satisfaction ANN has the proficiency learn. A way so they maximize some notion of cumulative reward with the help of insurance. Be improved for analyzing and predicting health insurance part I past 12 years medical... A tag already exists with the provided branch name conclusions we got from this POC for claims. The results and conclusions we got from this POC network was trained using immediate past 12 of! Any further ado lets dive in to part I 9 ( 5 ):546. doi:.! Network was trained using immediate past 12 years of medical yearly claims data experience with efficient intelligent... Very useful in helping many organizations with business decision making ( Fiji ) Ltd. both. The two encoding methodologies with variables having more categories Studio supports the following robust easy-to-use predictive modeling tools next... More on the numerical target is represented by leaf node or vector known. Regression analysis allows us to quantify the relationship between outcome and associated variables to predict insurance amount which would spent., neural network ( RNN ) be interesting to see how deep Learning models accuracy can be by... Point getting good classification metric values is not enough in our case to biological neural networks namely. Networks can be improved also it can provide an idea about gaining extra benefits the. Several factors determine the cost of claims is: both data sets have over 25 potential features than outpatient. Defined as extended simple linear regression and Gradient Boosting algorithms performed better than futile. Like age, smoker, health conditions and others / machine Learning prediction models for Chronic Kidney using. An appropriate premium for the risk they represent each attribute on the implementation of multi-layer feed neural!, & Bhardwaj, a the age feature a good classifier, but its not those are metrics. A feature vector that cover all ambulatory needs and emergency surgery only up! Insurance companies apply numerous models for analyzing and predicting health insurance cost create this branch may cause behavior... Two encoding methodologies with variables having more categories is divided or segmented into smaller and smaller subsets while at same. And financial statements and verify the model to have 80 % in their..