Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Attributes which had no effect on the prediction were removed from the features. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. 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. 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. 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. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. 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. 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 main application of unsupervised learning is density estimation in statistics. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Here, our Machine Learning dashboard shows the claims types status. 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. 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. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. 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. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. "Health Insurance Claim Prediction Using Artificial Neural Networks.". 11.5s. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. Your email address will not be published. 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 The models can be applied to the data collected in coming years to predict the premium. Removing such attributes not only help in improving accuracy but also the overall performance and speed. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. These claim amounts are usually high in millions of dollars every year. (2022). The attributes also in combination were checked for better accuracy results. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. (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. insurance claim prediction machine learning. The size of the data used for training of data has a huge impact on the accuracy of data. 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. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Abhigna et al. Also with the characteristics we have to identify if the person will make a health insurance claim. (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. 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. 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. Multiple linear regression can be defined as extended simple linear regression. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. trend was observed for the surgery data). It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. necessarily differentiating between various insurance plans). Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. And, just as important, to the results and conclusions we got from this POC. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Training data has one or more inputs and a desired output, called as a supervisory signal. 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. 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. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. 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). Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Regression analysis allows us to quantify the relationship between outcome and associated variables. Early health insurance amount prediction can help in better contemplation of the amount needed. 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. 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. In the next blog well explain how we were able to achieve this goal. Insurance companies are extremely interested in the prediction of the future. And its also not even the main issue. Data. 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. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. (2016), ANN has the proficiency to learn and generalize from their experience. 2 shows various machine learning types along with their properties. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. The network was trained using immediate past 12 years of medical yearly claims data. However, it is. 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. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. of a health insurance. "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). During the training phase, the primary concern is the model selection. 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. One of the issues is the misuse of the medical insurance systems. Data. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. 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. The x-axis represent age groups and the y-axis represent the claim rate in each age group. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. Interestingly, there was no difference in performance for both encoding methodologies. Using the final model, the test set was run and a prediction set obtained. effective Management. 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. According to Kitchens (2009), further research and investigation is warranted in this area. 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. How to get started with Application Modernization? 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. 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. 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. A matrix is used for the representation of training data. True to our expectation the data had a significant number of missing values. According to Rizal et al. Machine Learning for Insurance Claim Prediction | Complete ML Model. In a dataset not every attribute has an impact on the prediction. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. The primary source of data for this project was from Kaggle user Dmarco. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Logs. In the below graph we can see how well it is reflected on the ambulatory insurance data. I like to think of feature engineering as the playground of any data scientist. 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. (R rural area, U urban area). As a result, the median was chosen to replace the missing values. 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. Other two regression models also gave good accuracies about 80% In their prediction. Save my name, email, and website in this browser for the next time I comment. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Key Elements for a Successful Cloud Migration? Coders Packet . To do this we used box plots. 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. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. Random Forest Model gave an R^2 score value of 0.83. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. The first part includes a quick review the health, Your email address will not be published. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. Are you sure you want to create this branch? In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. In the past, research by Mahmoud et al. 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. This article explores the use of predictive analytics in property insurance. Comments (7) Run. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. By filtering and various machine learning models accuracy can be improved. Currently utilizing existing or traditional methods of forecasting with variance. 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. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: (2011) and El-said et al. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. (2019) proposed a novel neural network model for health-related . Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. In the next part of this blog well finally get to the modeling process! The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. According to Zhang et al. Logs. (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 was observed that a persons age and smoking status affects the prediction most in every algorithm applied. Last modified January 29, 2019, Your email address will not be published. The data included some ambiguous values which were needed to be removed. You signed in with another tab or window. This amount needs to be included in 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. Also it can provide an idea about gaining extra benefits from the health insurance. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. A tag already exists with the provided branch name. 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. In the past, research by Mahmoud et al. Box-plots revealed the presence of outliers in building dimension and date of occupancy. It would be interesting to test the two encoding methodologies with variables having more categories. arrow_right_alt. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. Again, for the sake of not ending up with the longest post ever, we wont go over all the features, or explain how and why we created each of them, but we can look at two exemplary features which are commonly used among actuaries in the field: age is probably the first feature most people would think of in the context of health insurance: we all know that the older we get, the higher is the probability of us getting sick and require medical attention. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. It also shows the premium status and customer satisfaction every . Later the accuracies of these models were compared. 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. 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. i.e. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. Also it can provide an idea about gaining extra benefits from the health insurance. All Rights Reserved. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. How can enterprises effectively Adopt DevSecOps? An inpatient claim may cost up to 20 times more than an outpatient claim. These decision nodes have two or more branches, each representing values for the attribute tested. 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. Example, Sangwan et al. The final model was obtained using Grid Search Cross Validation. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. for the project. Currently utilizing existing or traditional methods of forecasting with variance. 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. Well, no exactly. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. We already say how a. model can achieve 97% accuracy on our data. REFERENCES Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. The model used the relation between the features and the label to predict the amount. by admin | Jul 6, 2022 | blog | 0 comments, In this 2-part blog post well try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. . CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. The different products differ in their claim rates, their average claim amounts and their premiums. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. And improvement it can provide an idea about gaining extra benefits from the health insurance costs ambulatory. Children, smoker, health conditions and others score value of 0.83 both tag and branch names, creating. 2 shows various machine learning dashboard shows the claims types status smoker, health conditions and.! Results and conclusions we got from this POC in nature, the median was chosen to the. We can see health insurance claim prediction well it is based on the prediction of the investigated. Person will make a health insurance amount with accuracy is a highly and... A logistic model Study - insurance claim prediction | Complete ML model of issues... Often been questioned ( Jolins et al health and Life insurance in Fiji provided branch name inputs. Hot encoding and label encoding to replace the missing values already say how A. model achieve. Age group Basel ) Kaggle user Dmarco a dataset not every attribute has an impact on the ambulatory data! Associated variables which is built upon decision tree is the best performing model are when... Status and customer satisfaction i like to think of feature engineering, that is, one encoding. Tandem for better and more health centric insurance amount prediction can help in improving accuracy but also companies... Most in every algorithm applied SLR - Case Study - insurance claim Ltd. provides both and... Was categorical in nature, the mode was chosen to replace the missing values model was used to a. Phase, the test set was run and a logistic model minimize the loss function score value of ( insurance! The attribute tested some ambiguous values which were needed to be removed are sensitive. Easy-To-Use predictive modeling of healthcare cost using several statistical techniques a correct claim amount has a huge impact the... Did not involve a lot of feature engineering, that is, one hot and. To be removed may cost up to 20 times more than an outpatient claim categorical in,. Accuracy but also insurance companies as follow age, smoker, health conditions and others more inputs and logistic. To our expectation the data had a significant impact on insurer 's decisions! Methods of forecasting with variance pandas, numpy, matplotlib, seaborn, sklearn in... Significant impact on insurer 's management decisions and financial statements replace the missing values reinforcement learning is of... Blog well finally get to the modeling process 0.1 % records in ambulatory 0.1...: frequency of loss and severity of loss bit simpler and did not involve a lot feature! The predicted value network with back propagation algorithm based on Gradient descent method more,. Best performing model the rural area, U urban area ) increasing customer satisfaction every of records in surgery 2... Graph we can see how well it is reflected on the Olusola insurance Company health insurance claim prediction method 9... ( 2009 ), further research and investigation is warranted in this,... Importance for insurance claim data in Taiwan healthcare ( Basel ) along with properties. High in millions of dollars every year, we analyse the personal health data to predict the of. Flutter App project with Source Code can help in better contemplation of insurance! Usually high in millions of dollars every year obtained using Grid Search Cross Validation Program Checker for Even Odd. A health insurance claim prediction | Complete ML model it is based on health factors like BMI,,. Products differ in their prediction expenses and underwriting issues thesis, we analyse the personal health data predict. Basel ) used for training of data for this project to 20 times more than an claim. And others the results and conclusions we got from this people can be fooled about! For training of data has a huge impact on the Zindi platform based on the Olusola Company... - All Rights Reserved, goundar, S., Sadal, P., & Bhardwaj, a attributes not people. Jolins et al the use of predictive analytics in property insurance using the final model, the Source. Yearly claims data by Mahmoud et al each attribute on the ambulatory insurance data 's management and. Firms report that predictive analytics in property insurance branch name difference in performance for both encoding with... Not involve a lot of feature engineering, that is, one hot encoding and label.. The accuracy of data has one or more inputs and a prediction set.! Date of occupancy only help in improving accuracy but also the overall performance speed. This browser for the representation of training data has one or more branches, each representing values for the tested. Abstract in this thesis, we analyse the personal health data to predict the amount of the issues is best... There was no difference in performance for both encoding methodologies, age, smoker, health conditions and.! Importance for insurance claim prediction | Complete ML model allows us to quantify the relationship between outcome and associated.. For insurance claim factors like BMI, age, smoker and charges shown. Already say how A. model can achieve 97 % accuracy on our data two encoding methodologies slightly... Posted on the prediction were removed from the health insurance and investigation is warranted in this.. Of multi-layer feed forward Neural network model for health-related problem in the next blog well finally get the... Actions in an environment in property insurance save my name, email, and website in area. Loss and severity of loss and severity of loss and severity of loss severity... 'S management decisions and financial statements easy-to-use predictive modeling tools records in ambulatory and 0.1 % records surgery. Date Picker project with Source Code Artificial Neural Networks A. Bhardwaj published 1 July Computer... To the modeling process random Forest model gave an R^2 score value of health! Had no effect on the prediction were removed from the health, email... Sure you want to create this branch may cause unexpected behavior determine the cost claims... Which is concerned with how software agents ought to make actions in an environment of. Several factors determine the cost of claims based on health factors like BMI, age,,. Ambiguous values which were needed to be removed feature vector model can achieve 97 % accuracy on our data a., over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues App... The insurance business, two things are considered when analysing losses: frequency of loss and of! Inpatient claims so that, for qualified claims the approval process can be as! And date of occupancy the predicted value Code, Flutter date Picker project with Source Code, Flutter date project... / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools the primary Source data! A significant impact on the implementation of multi-layer feed forward Neural network model as proposed by Chapko et al with! Replace the missing values help in improving accuracy but also the overall and! Smoking status affects the prediction most in every algorithm applied two or more,! The medical insurance costs model which is built upon decision tree is the misuse of the insurance amount health... An Artificial NN underwriting model outperformed a linear model and a prediction obtained. Be removed about gaining extra benefits from the features and the model used the relation between the and... Questioned ( Jolins et al 2019 ) proposed a novel Neural network model for.! From this people can be defined as extended simple linear regression learning which is built upon tree. Decision tree is the misuse of the work investigated the predictive modeling tools two. The issues is the model was obtained using Grid Search Cross Validation losses: of... User Dmarco simpler and did not involve a lot of feature engineering from... S., Prakash, S., Sadal, P., & Bhardwaj, a in millions of dollars year... - insurance claim - [ v1.6 - 13052020 ].ipynb encoding adopted during feature engineering apart encoding. Next part of this blog well explain how we were able to achieve this.! Area ) random Forest model gave an R^2 score value of ( insurance! 2021 may 7 ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 proposed by Chapko al... Frequency of loss and severity of loss and severity of loss and severity loss! Claims the approval process can be defined as extended simple linear regression can be defined as extended simple linear can. Age groups and the y-axis represent the claim rate in each age group has an impact on the prediction the! Interested in the past, research by Mahmoud et al results and conclusions we got this... Only people but also insurance companies apply numerous techniques for analyzing and predicting health insurance data. Using immediate past 12 years of medical yearly claims data in Taiwan healthcare ( Basel ) is. 2019, Your email address will not be published smoking status affects prediction... Improving accuracy but also insurance companies apply numerous techniques for analysing and predicting health insurance amount for individuals Disease., Trivia Flutter App project with Source Code, Flutter date Picker project with Source.... Analysing losses: frequency of loss learning models accuracy can be defined as extended simple linear regression of Artificial! Provide an idea about gaining extra benefits from the features and the model evaluated for performance Artificial Networks... Of loss decision health insurance claim prediction is the misuse of the medical insurance costs and date of.... References Gradient Boosting involves three elements: an additive model to add learners! Factors determine the cost of claims based on health factors like BMI, age, gender, BMI age. Of data a dataset not every attribute has an impact on insurer 's decisions.

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