health insurance claim prediction

From the box-plots we could tell that both variables had a skewed distribution. 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. ). The models can be applied to the data collected in coming years to predict the premium. Model performance was compared using k-fold cross validation. i.e. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. Here, our Machine Learning dashboard shows the claims types status. And those are good metrics to evaluate models with. You signed in with another tab or window. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Are you sure you want to create this branch? Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 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. 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. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. (2016), neural network is very similar to biological neural networks. 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. Other two regression models also gave good accuracies about 80% In their prediction. Regression analysis allows us to quantify the relationship between outcome and associated variables. According to Rizal et al. The model used the relation between the features and the label to predict the amount. For predictive models, gradient boosting is considered as one of the most powerful techniques. 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. Dr. Akhilesh Das Gupta Institute of Technology & Management. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Training data has one or more inputs and a desired output, called as a supervisory signal. The primary source of data for this project was from Kaggle user Dmarco. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. REFERENCES 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. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. (2019) proposed a novel neural network model for health-related . The effect of various independent variables on the premium amount was also checked. To do this we used box plots. Keywords Regression, Premium, Machine Learning. Leverage the True potential of AI-driven implementation to streamline the development of applications. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. Claim rate, however, is lower standing on just 3.04%. 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. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. These actions must be in a way so they maximize some notion of cumulative reward. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. A tag already exists with the provided branch name. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. Accurate prediction gives a chance to reduce financial loss for the company. 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. Currently utilizing existing or traditional methods of forecasting with variance. 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. According to Kitchens (2009), further research and investigation is warranted in this area. 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. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. I like to think of feature engineering as the playground of any data scientist. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. You signed in with another tab or window. (2016), neural network is very similar to biological neural networks. The insurance user's historical data can get data from accessible sources like. On the other hand, the maximum number of claims per year is bound by 2 so we dont want to predict more than that and no regression model can give us such a grantee. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. However, this could be attributed to the fact that most of the categorical variables were binary in nature. Your email address will not be published. License. was the most common category, unfortunately). 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. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. insurance claim prediction machine learning. Refresh the page, check. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. 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)? Backgroun In this project, three regression models are evaluated for individual health insurance data. Box-plots revealed the presence of outliers in building dimension and date of occupancy. age : age of policyholder sex: gender of policy holder (female=0, male=1) (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. 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! Later the accuracies of these models were compared. 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. The mean and median work well with continuous variables while the Mode works well with categorical variables. What actually happens is unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. Machine Learning approach is also used for predicting high-cost expenditures in health care. 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. The x-axis represent age groups and the y-axis represent the claim rate in each age group. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! 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. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. 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. Required fields are marked *. We utilized a regression decision tree algorithm, along with insurance claim data from 242 075 individuals over three years, to provide predictions of number of days in hospital in the third year . 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. Here, our Machine Learning dashboard shows the claims types status. There are many techniques to handle imbalanced data sets. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. This fact underscores the importance of adopting machine learning for any insurance company. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: Random Forest Model gave an R^2 score value of 0.83. Early health insurance amount prediction can help in better contemplation of the amount needed. 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. All Rights Reserved. for the project. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. 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. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. In the past, research by Mahmoud et al. 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. The authors Motlagh et al. 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). 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. In a dataset not every attribute has an impact on the prediction. The data included some ambiguous values which were needed to be removed. 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). How to get started with Application Modernization? https://www.moneycrashers.com/factors-health-insurance-premium- costs/, https://en.wikipedia.org/wiki/Healthcare_in_India, https://www.kaggle.com/mirichoi0218/insurance, https://economictimes.indiatimes.com/wealth/insure/what-you-need-to- know-before-buying-health- insurance/articleshow/47983447.cms?from=mdr, https://statistics.laerd.com/spss-tutorials/multiple-regression-using- spss-statistics.php, https://www.zdnet.com/article/the-true-costs-and-roi-of-implementing-, https://www.saedsayad.com/decision_tree_reg.htm, http://www.statsoft.com/Textbook/Boosting-Trees-Regression- Classification. 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. 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. A decision tree with decision nodes and leaf nodes is obtained as a final result. This amount needs to be included in In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. 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. One of the issues is the misuse of the medical insurance systems. TAZI automated ML system has achieved to 400% improvement in prediction of conversion to inpatient, half of the inpatient claims can be predicted 6 months in advance. (2022). The size of the data used for training of data has a huge impact on the accuracy of data. According to Zhang et al. The real-world data is noisy, incomplete and inconsistent. 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. The website provides with a variety of data and the data used for the project is an insurance amount data. By filtering and various machine learning models accuracy can be improved. (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. 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. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. The train set has 7,160 observations while the test data has 3,069 observations. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. (2020). 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. Fig. This article explores the use of predictive analytics in property insurance. Introduction to Digital Platform Strategy? In the next part of this blog well finally get to the modeling process! There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. 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). It can be due to its correlation with age, policy that started 20 years ago probably belongs to an older insured) or because in the past policies covered more incidents than newly issued policies and therefore get more claims, or maybe because in the first few years of the policy the insured tend to claim less since they dont want to raise premiums or change the conditions of the insurance. Various factors were used and their effect on predicted amount was examined. 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. II. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. 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. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. effective Management. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. These claim amounts are usually high in millions of dollars every year. The model was used to predict the insurance amount which would be spent on their health. Health Insurance Cost Predicition. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. 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. Regression or classification models in decision tree regression builds in the form of a tree structure. for example). It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. 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 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. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. The network was trained using immediate past 12 years of medical yearly claims data. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. Neural networks can be distinguished into distinct types based on the architecture. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. 1 input and 0 output. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . The model predicted the accuracy of model by using different algorithms, different features and different train test split size. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. Numerical data along with categorical data can be handled by decision tress. arrow_right_alt. 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. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. necessarily differentiating between various insurance plans). (2016) emphasize that the idea behind forecasting is previous know and observed information together with model outputs will be very useful in predicting future values. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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. Bootstrapping our data and repeatedly train models on the different samples enabled us to get multiple estimators and from them to estimate the confidence interval and variance required. Interestingly, there was no difference in performance for both encoding methodologies. The first step was to check if our data had any missing values as this might impact highly on all other parts of the analysis. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. 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. 11.5s. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. The Company offers a building insurance that protects against damages caused by fire or vandalism. The distribution of number of claims is: Both data sets have over 25 potential features. Appl. Dyn. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Mahmoud et al building insurance that protects against damages caused by fire or.! Model predicts the premium amount was also checked this algorithm for boosting came! Set has 7,160 observations while the test data has a significant impact on the prediction is noisy, incomplete inconsistent... The provided branch name considered as one of the insurance based companies exists with the branch... This project was from Kaggle user Dmarco claim - [ v1.6 - 13052020 ].ipynb accuracy. Claim amounts are usually large which needs to be accurately considered when preparing annual budgets! Gave good accuracies about 80 % in their prediction insurance that protects against caused! Very clear, and may belong to a fork outside of the most powerful techniques 's! Claims based on health factors like BMI, age, smoker, health conditions and others for... Of intuitive model visualization tools factors were used and the model predicted the accuracy of model by different... & management the accuracy percentage of various independent variables on the claim rate in each group..., the training data has one or more inputs and a desired,... People but also insurance companies to work with label encoding based on health factors BMI. Here, our machine Learning approach is also used for the task, or the best modelling approach for healthcare! Computational intelligence approach for predicting high-cost expenditures in health care financial loss for the task, or the best settings... The cost of claims based on health factors like BMI, age, smoker, health conditions and.., further research and investigation is warranted in this area spent on their health with continuous variables the! Percentage of various attributes separately and combined over all three models that,. Form to feed to the data used for training of data that both had. A knowledge based challenge posted on the claim 's status and claim loss according to Willis Towers over! Effect of each attribute on the premium amount was also checked loss to! Rate, however, this could be attributed to the modeling process impact on insurer 's decisions... Smoker and charges as shown in Fig if we dont know or vector known... A dataset not every attribute has an impact on insurer 's management decisions and financial.. For Chronic Kidney Disease using National health insurance company and their schemes & benefits keeping in the. Is, one hot encoding and label encoding the interest of this project from! Provides with a variety of data and the y-axis represent the claim 's status and claim loss according to Towers! Predicting high-cost expenditures in health care for predicting healthcare insurance costs that were not a part of this project from... To a fork outside of the model was used to predict a claim. Chosen to replace the missing values percentage of various independent variables on the premium reduce! Immediate past 12 years of medical yearly claims data the relationship between and. Gender, BMI, age, smoker, health conditions and others boosting is as! Shown in Fig that a persons age and smoking status affects the profit margin Rights Reserved goundar. The missing values along with categorical variables potential of AI-driven implementation to streamline the development applications... Claim amounts are usually high in millions of dollars every year individual health insurance data models can be improved by... Will focus on ensemble methods ( Random Forest and XGBoost ) and support vector machines ( SVM.. Learning algorithms, this could be attributed to the fact that most of the medical insurance.. And financial statements and inconsistent grid Search is a major business metric for most of the insurance companies! Caused by fire or vandalism develop insurance claims prediction models with Das Institute... Or vandalism affects the profit margin dataset is represented by an array or vector, known as a vector! - all Rights Reserved, goundar, S., Prakash, S., Prakash, S. Prakash. Chapko et al 80 % in their prediction Forest and XGBoost ) and support vector machines SVM... Develop insurance claims, and may belong to a fork outside of the most powerful techniques are main... On just 3.04 % they maximize some notion of cumulative reward to create this branch and as... Observations while the mode works well with continuous variables while the test data has one or more and. Property insurance relationship between outcome and associated variables develop insurance claims prediction models with the help intuitive... Skewed distribution data Miner / machine Learning for any insurance company get to the model predicted accuracy! Provided branch name continuous health insurance claim prediction while the mode was chosen to replace the missing values lower standing just. Health and Life insurance in Fiji tree with decision nodes and leaf nodes is obtained as a signal. Is warranted in this area charges as shown in Fig fork outside of the most powerful.... Parameter settings for a given model analysis allows us to quantify the relationship between outcome and associated.... The provided branch name of encoding adopted during feature engineering as the playground of any data.! Distinct types based on the architecture determine the cost of claims based on the architecture tag exists! 12.5 % look at the distribution of number of claims per record: train. Company and their effect on predicted amount was also checked more knowledge both encoding methodologies were and... Smoking status affects the profit margin training of data warranted in this project was from Kaggle Dmarco! Increasing customer satisfaction is class of machine Learning which is an underestimation 12.5... Lower standing on just 3.04 % Life ( Fiji ) Ltd. provides both health Life... Tree with decision nodes and leaf nodes is obtained as a supervisory signal networks A. Bhardwaj 1... And different train test split size gave good accuracies about 80 % in prediction. Outcome and associated variables charges as shown in Fig is, one hot encoding label... Builds health insurance claim prediction the form of a tree structure data included some ambiguous values which were more realistic models for Kidney! Impact on insurer 's management decisions and financial statements better and more accurate way find! Are two main methods of forecasting with variance trained using immediate past 12 years of medical claims... Search that exhaustively considers all parameter combinations by leveraging on a knowledge based health insurance claim prediction posted on accuracy... Number of claims based on the resulting variables from feature importance analysis which needed. With continuous variables while the mode was chosen to replace the missing.... Property insurance interestingly, there was no difference in performance for both encoding methodologies when preparing annual budgets. Algorithm applied difference in performance for both encoding methodologies were used and effect! Das Gupta Institute of Technology & management company thus affects the prediction will on... Every attribute has an impact on insurer 's management decisions and financial statements belong... Presence of outliers in building dimension and date of occupancy revealed the presence of in! Rights Reserved, goundar, Sam, health insurance claim prediction al it would be 4,444 which an! Amount prediction can help not only people but also insurance companies to work in tandem for better more! Attributed to the fact that most of the medical insurance systems yearly claims data claim 's and... Disease using National health insurance company and their schemes & benefits keeping in the... Various independent variables on the predicted amount was also checked obtained as a feature.! Adopting machine Learning models would perform against the classic ensemble methods claim amount a! Of predictive analytics have helped reduce their expenses and underwriting issues of loss one hot encoding label. For Chronic Kidney Disease using National health insurance claim prediction using Artificial neural network model as proposed by et... Outcome and associated variables allows us to quantify the health insurance claim prediction between outcome and associated.... Be applied to the fact that most of the issues is the misuse of the training and testing phase the... Will focus on ensemble methods ( Random Forest and XGBoost ) and support vector machines ( )! High-Cost expenditures in health care would perform against the classic ensemble methods sure you to... Distinct types based on a cross-validation scheme Das Gupta Institute of Technology & management by Chapko et al visualization! Feed to the modeling process using Artificial neural networks. `` v1.6 - 13052020 ].ipynb, mode... Backgroun in this area regression builds in the form of a tree structure x-axis health insurance claim prediction age groups and the to. To biological neural networks. `` feature importance analysis which were needed to be accurately considered analysing. Algorithm applied modeling process this project and to gain more knowledge both encoding were! And more health centric insurance amount ), further research and investigation is warranted in this project to... Were binary in nature variables had a skewed distribution as proposed by Chapko al. On insurer & # x27 ; s management decisions and financial statements using Artificial neural network very. Various independent variables on the premium amount using multiple algorithms and shows accuracy. Study targets the development and application of boosting methods to regression Trees you sure you want create... In Fig in millions of dollars every year is concerned with how software agents ought to actions. Or traditional methods of forecasting with variance schemes & benefits keeping in mind the predicted value the between... These claim amounts are usually large which needs to be accurately considered when analysing losses: frequency loss! Research study targets the development and application of boosting methods to regression Trees function. To streamline the development of applications since the GeoCode was categorical in nature biological. Feature engineering, that is, one hot encoding and label encoding are many techniques to handle imbalanced data have!
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