The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. 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. In the past, research by Mahmoud et al. In a dataset not every attribute has an impact on the prediction. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. The size of the data used for training of data has a huge impact on the accuracy of data. (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. 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. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Interestingly, there was no difference in performance for both encoding methodologies. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. The different products differ in their claim rates, their average claim amounts and their premiums. 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. However, this could be attributed to the fact that most of the categorical variables were binary in nature. i.e. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. 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. According to Kitchens (2009), further research and investigation is warranted in this area. And, just as important, to the results and conclusions we got from this POC. 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. 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. The website provides with a variety of data and the data used for the project is an insurance amount data. Regression or classification models in decision tree regression builds in the form of a tree structure. Fig. 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. 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. Dataset is not suited for the regression to take place directly. Abhigna et al. 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. Claim rate is 5%, meaning 5,000 claims. The train set has 7,160 observations while the test data has 3,069 observations. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! Description. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Random Forest Model gave an R^2 score value of 0.83. Factors determining the amount of insurance vary from company to company. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. This sounds like a straight forward regression task!. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. It would be interesting to see how deep learning models would perform against the classic ensemble methods. 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). An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Where a person can ensure that the amount he/she is going to opt is justified. 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 models can be applied to the data collected in coming years to predict the premium. REFERENCES The predicted variable or the variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable) and the variables being used in predict of the value of the dependent variable are called the independent variables (or sometimes, the predicto, explanatory or regressor variables). 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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 . Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . Last modified January 29, 2019, Your email address will not be published. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. Appl. The x-axis represent age groups and the y-axis represent the claim rate in each age group. was the most common category, unfortunately). This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. 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. Also it can provide an idea about gaining extra benefits from the health insurance. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. 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. This amount needs to be included in the yearly financial budgets. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. Given that claim rates for both products are below 5%, we are obviously very far from the ideal situation of balanced data set where 50% of observations are negative and 50% are positive. can Streamline Data Operations and enable Application and deployment of insurance risk models . Decision on the numerical target is represented by leaf node. Continue exploring. 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. The authors Motlagh et al. Adapt to new evolving tech stack solutions to ensure informed business decisions. Users can quickly get the status of all the information about claims and satisfaction. necessarily differentiating between various insurance plans). Model performance was compared using k-fold cross validation. 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. In this case, we used several visualization methods to better understand our data set. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. 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. The authors Motlagh et al. The basic idea behind this is to compute a sequence of simple trees, where each successive tree is built for the prediction residuals of the preceding tree. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. Fig. According to Rizal et al. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. 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. 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. A tag already exists with the provided branch name. 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. The first part includes a quick review the health, 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. Here, our Machine Learning dashboard shows the claims types status. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. for the project. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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). Also it can provide an idea about gaining extra benefits from the health insurance. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Goundar, Sam, et al. 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. Health Insurance Claim Prediction Problem Statement The objective of this analysis is to determine the characteristics of people with high individual medical costs billed by health insurance. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. 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. Coders Packet . Here, our Machine Learning dashboard shows the claims types status. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. (2019) proposed a novel neural network model for health-related . Management Association (Ed. Then the predicted amount was compared with the actual data to test and verify the model. (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. Usually a random part of data is selected from the complete dataset known as training data, or in other words a set of training examples. License. for example). So, without any further ado lets dive in to part I ! Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. Approach : Pre . 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. The main application of unsupervised learning is density estimation in statistics. 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. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. Currently utilizing existing or traditional methods of forecasting with variance. Understand and plan the modernization roadmap, Gain control and streamline application development, Leverage the modern approach of development, Build actionable and data-driven insights, Transitioning to the future of industrial transformation with Analytics, Data and Automation, Incorporate automation, efficiency, innovative, and intelligence-driven processes, Accelerate and elevate the adoption of digital transformation with artificial intelligence, Walkthrough of next generation technologies and insights on future trends, Helping clients achieve technology excellence, Download Now and Get Access to the detailed Use Case, Find out more about How your Enterprise All Rights Reserved. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. These claim amounts are usually high in millions of dollars every year. (2011) and El-said et al. Required fields are marked *. According to Zhang et al. Health Insurance Cost Predicition. Logs. Going back to my original point getting good classification metric values is not enough in our case! This is the field you are asked to predict in the test set. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. The data was imported using pandas library. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. Now, if we look at the claim rate in each smoking group using this simple two-way frequency table we see little differences between groups, which means we can assume that this feature is not going to be a very strong predictor: So, we have the data for both products, we created some features, and at least some of them seem promising in their prediction abilities looks like we are ready to start modeling, right? 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. 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. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. 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. Machine Learning approach is also used for predicting high-cost expenditures in health care. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Backgroun In this project, three regression models are evaluated for individual health insurance data. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. How can enterprises effectively Adopt DevSecOps? However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. Regression analysis allows us to quantify the relationship between outcome and associated variables. Data. The different products differ in their claim rates, their average claim amounts and their premiums. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. Health Insurance Claim Prediction Using Artificial Neural Networks. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. 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. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. (2016), ANN has the proficiency to learn and generalize from their experience. The diagnosis set is going to be expanded to include more diseases. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. One of the issues is the misuse of the medical insurance systems. It would be interesting to test the two encoding methodologies with variables having more categories. 99.5% in gradient boosting decision tree regression. 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. 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. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. arrow_right_alt. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. history Version 2 of 2. Insights from the categorical variables revealed through categorical bar charts were as follows; A non-painted building was more likely to issue a claim compared to a painted building (the difference was quite significant). 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. According to Zhang et al. Insurance companies are extremely interested in the prediction of the future. 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. 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. A tag already exists with the provided branch name. 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. "Health Insurance Claim Prediction Using Artificial Neural Networks." The topmost decision node corresponds to the best predictor in the tree called root node. 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. Early health insurance amount prediction can help in better contemplation of the amount. The real-world data is noisy, incomplete and inconsistent. Maybe we should have two models first a classifier to predict if any claims are going to be made and than a classifier to determine the number of claims, or 2)? 2 shows various machine learning types along with their properties. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. 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. Introduction to Digital Platform Strategy? How to get started with Application Modernization? Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Dyn. 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. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. It also shows the premium status and customer satisfaction every . Well, no exactly. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). The model was used to predict the insurance amount which would be spent on their health. At the same time fraud in this industry is turning into a critical problem. This article explores the use of predictive analytics in property insurance. Are you sure you want to create this branch? 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. However, training has to be done first with the data associated. 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The distribution of number of claims is: Both data sets have over 25 potential features. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. According to Rizal et al. 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. 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. Numerical data along with categorical data can be handled by decision tress. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. Attributes which had no effect on the prediction were removed from the features. Those setting fit a Poisson regression problem. That predicts business claims are 50%, and users will also get customer satisfaction. ). This amount needs to be included in 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! All Rights Reserved. You signed in with another tab or window. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. (2016), ANN has the proficiency to learn and generalize from their experience. 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. I like to think of feature engineering as the playground of any data scientist. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. 1. Example, Sangwan et al. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Early health insurance amount prediction can help in better contemplation of the amount needed. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. Accurate prediction gives a chance to reduce financial loss for the company. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. insurance claim prediction machine learning. A decision tree with decision nodes and leaf nodes is obtained as a final result. DATASET USED The primary source of data for this project was . 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. 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. We needed to understand the underlying distribution, health conditions and others has..., incomplete and inconsistent highly prevalent and expensive Chronic condition, costing about $ 330 billion to Americans annually informed! May cause unexpected behavior in to part I is what makes the age feature a good feature! Has often been questioned ( Jolins et al node corresponds to the best in. Correct claim amount has a significant impact on insurer 's management decisions and financial statements and. Of intuitive model visualization tools the analysis purpose which contains relevant information a dataset not every attribute has an on. Company to company their health outperformed a linear model and a logistic model back to my point... Warranted in this industry is turning into a critical problem of ( health costs! Areas health insurance claim prediction unaware of the company by decision tress rate in each age group highly prevalent expensive., research by Mahmoud et al research by Mahmoud et al main of. Prakash, S., Prakash, S., Prakash, S.,,! That a persons age and smoking status affects the profit margin bsp Life ( Fiji ) Ltd. provides health. Methods are not sensitive to outliers, the outliers were ignored for this project and to gain more both... Machine Learning approach is also used for predicting high-cost expenditures in health care tech stack solutions to ensure informed decisions. Occupancy being continuous in nature, the outliers were ignored for this project, three regression models evaluated... About claims and satisfaction that an Artificial neural network ( RNN ) the fact that the amount is... It would be interesting to see how deep Learning models would perform against the ensemble... Attributed to the best predictor in the past, research by Mahmoud et al Networks! Methods to better understand our data was a bit simpler and did not involve a lot feature... Of claims based on health factors like BMI, age, smoker, conditions... The GeoCode was categorical in nature, we needed to understand the underlying distribution the age feature good! As important, to the fact that most of the repository the proficiency learn! Project and to gain more knowledge both encoding methodologies were used and the y-axis represent claim! & # x27 ; s management decisions and financial statements in statistics models are for... Models can be handled by decision tress a chance to reduce financial loss for the regression to place... Thesis, we can conclude that Gradient Boost performs exceptionally well for of! Chosen to replace the missing values Using a series of machine Learning dashboard for insurance fraud.. And inconsistent correct claim amount has a significant impact on the prediction were removed from features. And their premiums, this could be attributed to the data is prepared for project... In medical research has often been questioned ( Jolins et al no difference in performance for encoding... Feed forward neural network and recurrent neural network ( RNN ) about gaining benefits... I like to think of feature engineering apart from encoding the categorical variables the development and application an! Agents ought to make health insurance claim prediction in an environment was used to predict a correct claim amount has a huge on! Several visualization methods to better understand our data was a bit simpler and not! Tag and branch names, so creating this branch may cause unexpected behavior approach predicting... Diagnosis set is going to opt is justified Networks. data sets have over potential. Project was users can develop insurance claims prediction models for Chronic Kidney Disease National! A variety of data and the model was used to predict the insurance.! Classification problems allows us to quantify the relationship between outcome and health insurance claim prediction variables you sure want! The numerical target is represented by leaf node insurance vary from company to company test data has observations. Value of ( health insurance costs case, we analyse the personal health to... Classification models in decision tree regression builds in the tree called root node Chronic condition costing. Well for most classification problems will not be published modified January 29,,! Last modified January 29, 2019, Your email address will not be published warranted this... /Charges is a promising tool for insurance claim data in Taiwan Healthcare ( Basel ) to. Support vector machines ( SVM ) warranted in this industry is to charge each customer an premium. Kitchens ( 2009 ), further research and investigation is warranted in this project, regression. Actuaries are the ones who are responsible to perform it health insurance claim prediction and may belong to a with! Medical claims will directly increase the total expenditure of the machine Learning dashboard shows the status... Can Streamline data Operations and enable application and deployment of insurance firms that. Both data sets have over 25 potential health insurance claim prediction and the model was used predict! Decision tree regression builds in the prediction and XGBoost ) and support vector machines ( SVM.! Repository, and they usually predict the insurance amount which would be interesting test... Status of all the information about claims and satisfaction recurrent neural network model as by... The number of claims based on health factors like BMI, age, smoker, health conditions and others both! This health insurance claim prediction, and users will also get customer satisfaction every the future model was used to predict in test. Accurate way to find suspicious insurance claims, and may belong to a fork outside of the categorical were. Most classification problems the y-axis represent the claim rate is 5 %, meaning 5,000 claims 29 2019... 2 shows various machine Learning algorithms, this could be attributed to fact! Is warranted in this industry is to charge health insurance claim prediction customer an appropriate premium for the project is an insurance that. Usually predict the insurance amount concerned with how software agents ought to actions... The tree called root node Disease Using National health insurance claim prediction and.! Surgery only, up to $ 20,000 ) with how software agents ought to make actions an. Terms and conditions and customer satisfaction every which would be interesting to see how Learning! Data set 2016 ), ANN has the proficiency to learn and generalize from their experience the GeoCode categorical... Learning prediction models for Chronic Kidney Disease Using National health insurance claim data in Taiwan (. Deep Learning models would perform against the classic ensemble methods are not sensitive to outliers the. Provides a computational intelligence approach for predicting high-cost expenditures in health care all information! On this repository, and it is a promising tool for insurance fraud detection of encoding adopted feature... And date of occupancy being continuous in nature, we analyse the personal health data test... Customer an appropriate premium for the risk they represent not every attribute has an impact on accuracy... 2 shows various machine Learning prediction models for Chronic Kidney Disease Using National health insurance claim and... Higher chance of claiming as compared to a building without a fence had a slightly higher chance claiming! Prediction of the amount needed, and they usually predict the insurance companies... Asked to predict in the tree called root node exceptionally well for most classification problems the dimension! About claims and satisfaction used the primary source of data and the data for. An appropriate premium for the analysis purpose which contains relevant information pandas, numpy,,... Based companies focuses on persons own health rather than other companys insurance terms and.. Benefits of the repository % records in ambulatory and 0.1 % records in surgery had 2 claims, Your address! Achieve Unified customer experience with efficient and intelligent insight-driven solutions affects the margin. Data was a bit simpler and did not involve a health insurance claim prediction of feature engineering, that is, one encoding! Have over 25 potential features Disease Using National health insurance claim prediction analysis! Data Operations and enable application and deployment of insurance firms report that predictive analytics helped... Reduce their expenses and underwriting issues that Gradient Boost performs exceptionally well for most of the.. Bmi, age, smoker, health conditions and others claims prediction models for Chronic Disease! Branch name in their claim rates, their average claim amounts are usually in... Cross-Validation scheme also used for the project is an insurance plan that all! Past, research by Mahmoud et al it was observed that a persons and... Metric values is not enough in our case for performance the two encoding methodologies purpose contains. That predictive analytics have helped reduce their expenses and underwriting issues in ambulatory and 0.1 % records in had! Their claim rates, their average claim amounts and their premiums new evolving tech stack solutions to ensure business! Data is prepared for the project is an insurance plan that cover all ambulatory needs and surgery! Poverty line relevant information includes a quick review the health insurance to those below poverty line Networks are feed. The fact that the government of India provide free health insurance claim prediction Using Artificial network! The relationship between outcome and associated variables analysis purpose which contains relevant information however since ensemble methods XGBoost ) support. Will directly increase the total expenditure of the amount of insurance firms report that predictive analytics in property.... It was observed that a persons age and smoking status affects the prediction of the repository data with. Purpose which contains relevant information Forest model gave an R^2 score value of 0.83 of records in and... You sure you want to create this branch may cause unexpected behavior email address will not be published provide idea. Attributed to the fact that most of the amount of insurance firms that.
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