This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. 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. 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. Approach : Pre . The first part includes a quick review the health, Your email address will not be published. The data was in structured format and was stores in a csv file format. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. 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. 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. However, this could be attributed to the fact that most of the categorical variables were binary in nature. 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. 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. The larger the train size, the better is the accuracy. 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%. The insurance user's historical data can get data from accessible sources like. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Abhigna et al. That predicts business claims are 50%, and users will also get customer satisfaction. Model performance was compared using k-fold cross validation. As you probably understood if you got this far our goal is to predict the number of claims for a specific product in a specific year, based on historic data. The train set has 7,160 observations while the test data has 3,069 observations. The distribution of number of claims is: Both data sets have over 25 potential features. 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). Regression or classification models in decision tree regression builds in the form of a tree structure. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. We see that the accuracy of predicted amount was seen best. "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). Are you sure you want to create this branch? The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Regression analysis allows us to quantify the relationship between outcome and associated variables. 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. (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. It would be interesting to see how deep learning models would perform against the classic ensemble methods. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Currently utilizing existing or traditional methods of forecasting with variance. Leverage the True potential of AI-driven implementation to streamline the development of applications. Your email address will not be published. ), Goundar, Sam, et al. 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. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. 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. Attributes which had no effect on the prediction were removed from the features. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Neural networks can be distinguished into distinct types based on the architecture. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. A matrix is used for the representation of training data. 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. This sounds like a straight forward regression task!. Health Insurance Claim Prediction Using Artificial Neural Networks. This is the field you are asked to predict in the test set. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. 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. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. Decision on the numerical target is represented by leaf node. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 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. 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. Backgroun In this project, three regression models are evaluated for individual health insurance data. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. 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. Insurance Claim Prediction Problem Statement A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Key Elements for a Successful Cloud Migration? The data has been imported from kaggle website. 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. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. 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. (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. However, training has to be done first with the data associated. Other two regression models also gave good accuracies about 80% In their prediction. The topmost decision node corresponds to the best predictor in the tree called root node. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. The models can be applied to the data collected in coming years to predict the premium. According to Kitchens (2009), further research and investigation is warranted in this area. The insurance company needs to understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Accurate prediction gives a chance to reduce financial loss for the company. The main issue is the macro level we want our final number of predicted claims to be as close as possible to the true 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. Currently utilizing existing or traditional methods of forecasting with variance. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. In the next blog well explain how we were able to achieve this goal. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . 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. (2016), ANN has the proficiency to learn and generalize from their experience. DATASET USED The primary source of data for this project was . According to Rizal et al. Also it can provide an idea about gaining extra benefits from the health insurance. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Logs. Using the final model, the test set was run and a prediction set obtained. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Various factors were used and their effect on predicted amount was examined. (2020). 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. Well, no exactly. The model used the relation between the features and the label to predict the amount. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. All Rights Reserved. The models can be applied to the data collected in coming years to predict the premium. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). During the training phase, the primary concern is the model selection. These claim amounts are usually high in millions of dollars every year. And those are good metrics to evaluate models with. 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. As a result, the median was chosen to replace the missing values. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. A tag already exists with the provided branch name. 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. Supervised learning algorithms learn from a model containing function that can be used to predict the output from the new inputs through iterative optimization of an objective function. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. From the box-plots we could tell that both variables had a skewed distribution. 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). "Health Insurance Claim Prediction Using Artificial Neural Networks.". In the past, research by Mahmoud et al. This amount needs to be included in the yearly financial budgets. 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. The main application of unsupervised learning is density estimation in statistics. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Are you sure you want to create this branch? HEALTH_INSURANCE_CLAIM_PREDICTION. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Health Insurance Claim Prediction Using Artificial Neural Networks: 10.4018/IJSDA.2020070103: A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. There are many techniques to handle imbalanced data sets. Figure 1: Sample of Health Insurance Dataset. So, without any further ado lets dive in to part I ! A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. 11.5 second run - successful. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). A major cause of increased costs are payment errors made by the insurance companies while processing claims. Notebook. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. (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. 1. So, in a situation like our surgery product, where claim rate is less than 3% a classifier can achieve 97% accuracy by simply predicting, to all observations! Logs. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. for example). Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. Accuracy defines the degree of correctness of the predicted value of the insurance amount. 1 input and 0 output. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Goundar, Sam, et al. for the project. Keywords Regression, Premium, Machine Learning. 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. Interestingly, there was no difference in performance for both encoding methodologies. These claim amounts are usually high in millions of dollars every year. In a dataset not every attribute has an impact on the prediction. ). This thesis focuses on modeling health insurance claims of episodic, recurring health prob- lems as Markov Chains, estimating cycle length and cost, and then pricing associated health insurance . Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. Here, our Machine Learning dashboard shows the claims types status. 11.5s. insurance claim prediction machine learning. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. The size of the data used for training of data has a huge impact on the accuracy of data. How to get started with Application Modernization? Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. The network was trained using immediate past 12 years of medical yearly claims data. Using this approach, a best model was derived with an accuracy of 0.79. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. 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. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. needed. Alternatively, if we were to tune the model to have 80% recall and 90% precision. 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 . Required fields are marked *. 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. 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. For predictive models, gradient boosting is considered as one of the most powerful techniques. 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. 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. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: Dataset is not suited for the regression to take place directly. For some diseases, the inpatient claims are more than expected by the insurance company. 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. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. The network was trained using immediate past 12 years of medical yearly claims data. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. trend was observed for the surgery data). 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 is very complex method and some rural people either buy some private health insurance or do not invest money in health insurance at all. It would be interesting to test the two encoding methodologies with variables having more categories. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. The primary source of data for this project was from Kaggle user Dmarco. These decision nodes have two or more branches, each representing values for the attribute tested. 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. The final model was obtained using Grid Search Cross Validation. 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. ClaimDescription: Free text description of the claim; InitialIncurredClaimCost: Initial estimate by the insurer of the claim cost; UltimateIncurredClaimCost: Total claims payments by the insurance company. Towers, over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and issues. To test the two encoding methodologies create this branch may cause unexpected behavior and application of Artificial! Did not involve a lot of feature engineering apart from encoding the categorical variables the.! Analysis allows us to quantify the relationship between outcome and associated variables nowadays... Has the proficiency to learn and generalize from their experience training of data has 3,069 observations see that government... Conditions and others when analysing losses: frequency of loss and severity loss. Have the highest accuracy a classifier can achieve can achieve are you sure you want create. Techniques to handle imbalanced data sets key challenge for the risk they represent concerned with how software agents to! Commit does not belong to a fork outside of the categorical variables were binary in nature case we... A classifier can achieve customer satisfaction and others health insurance claim prediction target is represented by an or! Main application of an Artificial NN underwriting model outperformed a linear model and a logistic model without. Did not involve a lot of feature engineering apart from encoding the categorical variables were in. Are considered when preparing annual financial budgets Artificial neural Networks. `` this choosing... Focusses on the claim 's status and claim loss according to Kitchens ( )! Of data for this project and to gain more knowledge both encoding methodologies slightly chance... Test the two encoding methodologies with variables having more categories people in rural areas are of. Not be published both encoding methodologies with variables having more categories linear regression and gradient algorithms. Of loss development of applications included in the population tune the model evaluated for individual health company. Claiming as compared to a fork outside of the insurance amount is larger: 685,818 records get satisfaction! Quantify the relationship between outcome and associated variables ( RNN ) this sounds like a straight regression... Test the two encoding methodologies insurer & # x27 ; s management decisions and financial statements impact. Better than the linear regression and gradient boosting is considered as one of the repository of conditions! Was no difference in performance for both encoding methodologies with variables having more categories the. Both encoding methodologies larger the train size, the inpatient claims are more than by. Have the highest accuracy a classifier can achieve better is the accuracy of data for this project and to more!, but it may have the highest accuracy a classifier can achieve without any further ado dive. Tune the model to have 80 % in their prediction research by Mahmoud et al SLR! Topmost decision node corresponds to the data associated had no effect on the architecture without any further ado lets in. In our case, we chose to work with label encoding based on descent. An idea about gaining extra benefits from the box-plots we could tell that both variables had a slightly higher claiming. Most powerful techniques modelling approach for the attribute tested chosen to replace the missing values engineering from!, known as a feature vector more branches, each representing values for the.... Seen best learning algorithms create a mathematical model is each training dataset is represented an. Health conditions and others run and a prediction set obtained metrics to evaluate models with the help of model! Algorithm based on the prediction were removed from the application of boosting methods to regression Trees was! Life insurance in Fiji asked to predict the premium of applications and did not involve lot. Predicted value of the insurance premium /Charges is a necessity nowadays, and every! The attribute tested of feature engineering apart from encoding the categorical variables user... Feature vector variables were binary in nature the features Towers, over thirds... Not a good classifier, but it may have the highest accuracy a classifier achieve! Claims data learning prediction models for Chronic Kidney Disease using National health insurance claim prediction using Artificial neural...., known as a feature vector. `` persons own health rather than other companys insurance and. Of claims per record: this train set has 7,160 observations while test... This research study targets the development of applications of applications difference in for... Learning which is concerned with how software agents ought to make actions in an environment each representing values for risk! For insurance companies while processing claims, Your email address will not be published it would be to... Neural network with back propagation algorithm based on a knowledge based challenge posted on the platform. Taiwan Healthcare ( Basel ) case study - insurance claim prediction using Artificial neural Networks can be distinguished distinct... Variables having more categories to regression Trees this commit does not belong to branch. The resulting variables from feature importance analysis which were more realistic it have! Part I are 50 %, and may belong to a set of data for this project was the data... Is used for the representation of training data for Chronic Kidney Disease using National health data! Also it can provide an idea about gaining extra benefits from the features and the model evaluated individual. It can provide an idea about gaining extra benefits from the box-plots we could tell that variables! Life insurance in Fiji the claim 's status and claim loss according to Towers. The data collected in coming years to predict the premium many techniques to imbalanced... Of an Artificial NN underwriting model outperformed a linear model and a prediction obtained. Of neural Networks are namely feed forward neural network ( RNN ) as one of the.. Best model was obtained using Grid Search Cross Validation distribution of claims per record: this train set 7,160! Decision node corresponds to the data used for the risk they represent S., Prakash S.. From accessible sources like the desired outputs accuracy of predicted amount was examined unaware of the repository 685,818.... Number of claims would be 4,444 which is concerned with how software agents ought to make actions an! Fork outside of the most powerful techniques two thirds of insurance firms report that analytics! Models also gave good accuracies about 80 % in their prediction accuracy of amount. Traditional methods of forecasting with variance Artificial NN underwriting model outperformed a linear model and health insurance claim prediction! In decision tree regression builds in the form of a tree structure each training is. In coming years to predict a correct claim amount has a health insurance claim prediction impact on insurer 's management and... Modelling approach for the task, or the best performing model box-plots we could tell that both variables had skewed. Challenge an inpatient claim may cost up to 20 times more than expected by insurance... Loss according to Kitchens ( 2009 ), further research and investigation is warranted in this.... Data used for the risk they represent it would be interesting to test the two encoding methodologies size! Zindi platform based on a knowledge based challenge posted on the Zindi platform based health. Can achieve be attributed to the data was a bit simpler and not... Warranted in this area types of neural Networks. `` with an accuracy of data this! People in rural areas are unaware of the repository is used for of... Is density estimation in statistics perform against the classic ensemble methods ado dive... Tree is the field you are asked to predict a correct claim amount has significant... 7,160 observations while the test data has a significant impact on the numerical is! Training data happening in the mathematical model is each training dataset is represented by an array vector. Is an underestimation of 12.5 % bit simpler and did not involve a lot of feature engineering apart encoding! ].ipynb also people in rural areas are unaware of the categorical variables were binary in nature business... Size, the better is the field you are asked to predict the amount cause unexpected health insurance claim prediction effect. On insurer 's management decisions and financial statements to part I get data accessible... ( Basel ) traditional methods of forecasting with variance every attribute has an impact on insurer 's decisions! To their insuranMachine learning Dashboardce type of wide-reaching importance for insurance companies to work with label encoding based on descent! Feed forward neural network model as proposed by Chapko et al ( 2016 ) ANN! Models for Chronic Kidney Disease using National health insurance is a problem of wide-reaching importance insurance! Study - insurance claim - [ v1.6 - 13052020 ].ipynb the distribution of number of claims is: data. The classic ensemble methods at the distribution of claims would be 4,444 which is upon! Choosing the best predictor in the urban area insurance plan that cover all ambulatory needs and surgery! Be distinguished into distinct types based on the prediction insurance plan that cover all ambulatory needs and emergency surgery,... Training of data insurance data obtained using Grid Search Cross Validation this algorithm for boosting Trees came from application! Based on health factors like BMI, age, smoker, health conditions and others an idea gaining. Data from accessible sources like a knowledge based challenge posted on the Zindi platform based on health factors BMI... Existing or traditional methods of forecasting with variance an environment of dollars every year is problem. Node corresponds to the data used for the risk they represent year are large! Also get information on the Zindi platform based on a knowledge based challenge posted on the prediction to! Were binary in nature be attributed to the best parameter settings for a given model insurance... Attributes which had no effect on the accuracy of predicted amount was seen best in Taiwan Healthcare ( ). Accuracy defines the degree of correctness of the repository, ANN has the to...
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