health insurance claim prediction

Here, our Machine Learning dashboard shows the claims types status. DATASET USED The primary source of data for this project was . Application and deployment of insurance risk models . Fig. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. The models can be applied to the data collected in coming years to predict the premium. The mean and median work well with continuous variables while the Mode works well with categorical variables. Leverage the True potential of AI-driven implementation to streamline the development of applications. I like to think of feature engineering as the playground of any data scientist. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. Health Insurance Claim Prediction Using Artificial Neural Networks. 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 . Decision on the numerical target is represented by leaf node. numbers were altered by the same factor in order to enhance confidentiality): 568,260 records in the train set with claim rate of 5.26%. The topmost decision node corresponds to the best predictor in the tree called root node. Example, Sangwan et al. The authors Motlagh et al. We had to have some kind of confidence intervals, or at least a measure of variance for our estimator in order to understand the volatility of the model and to make sure that the results we got were not just. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. Other two regression models also gave good accuracies about 80% In their prediction. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Alternatively, if we were to tune the model to have 80% recall and 90% precision. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. ), Goundar, Sam, et al. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. 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. Well, no exactly. 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. 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. Pre-processing and cleaning of data are one of the most important tasks that must be one before dataset can be used for machine learning. the last issue we had to solve, and also the last section of this part of the blog, is that even once we trained the model, got individual predictions, and got the overall claims estimator it wasnt enough. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. This feature may not be as intuitive as the age feature why would the seniority of the policy be a good predictor to the health state of the insured? Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. (2020). All Rights Reserved. Fig. 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). 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. ). Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. necessarily differentiating between various insurance plans). A research by Kitchens (2009) is a preliminary investigation into the financial impact of NN models as tools in underwriting of private passenger automobile insurance policies. In the next part of this blog well finally get to the modeling process! BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. According to Zhang et al. 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. The full process of preparing the data, understanding it, cleaning it and generate features can easily be yet another blog post, but in this blog well have to give you the short version after many preparations we were left with those data sets. Are you sure you want to create this branch? 1 input and 0 output. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. The attributes also in combination were checked for better accuracy results. J. Syst. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. 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. 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. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. 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. For predictive models, gradient boosting is considered as one of the most powerful techniques. Are you sure you want to create this branch? Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. From the box-plots we could tell that both variables had a skewed distribution. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. 1. Figure 1: Sample of Health Insurance Dataset. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Where a person can ensure that the amount he/she is going to opt is justified. The model used the relation between the features and the label to predict the amount. for the project. A comparison in performance will be provided and the best model will be selected for building the final model. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. Settlement: Area where the building is located. The authors Motlagh et al. The final model was obtained using Grid Search Cross Validation. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. According to IBM, Exploratory Data Analysis (EDA) is an approach used by data scientists to analyze data sets and summarize their main characteristics by mainly employing visualization methods. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. (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. And its also not even the main issue. 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)? This amount needs to be included in the yearly financial budgets. The website provides with a variety of data and the data used for the project is an insurance amount data. In the past, research by Mahmoud et al. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. 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. 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. Building Dimension: Size of the insured building in m2, Building Type: The type of building (Type 1, 2, 3, 4), Date of occupancy: Date building was first occupied, Number of Windows: Number of windows in the building, GeoCode: Geographical Code of the Insured building, Claim : The target variable (0: no claim, 1: at least one claim over insured period). Machine Learning for Insurance Claim Prediction | Complete ML Model. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Insurance companies are extremely interested in the prediction of the future. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. Dyn. Key Elements for a Successful Cloud Migration? insurance claim prediction machine learning. 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). Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). The model was used to predict the insurance amount which would be spent on their health. An inpatient claim may cost up to 20 times more than an outpatient claim. Example, Sangwan et al. Data. 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. A major cause of increased costs are payment errors made by the insurance companies while processing claims. (2016), neural network is very similar to biological neural networks. Claim rate, however, is lower standing on just 3.04%. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! Using feature importance analysis the following were selected as the most relevant variables to the model (importance > 0) ; Building Dimension, GeoCode, Insured Period, Building Type, Date of Occupancy and Year of Observation. Box-plots revealed the presence of outliers in building dimension and date of occupancy. However, training has to be done first with the data associated. The data has been imported from kaggle website. These claim amounts are usually high in millions of dollars every year. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. history Version 2 of 2. According to Kitchens (2009), further research and investigation is warranted in this area. This article explores the use of predictive analytics in property 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. 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. Data. We treated the two products as completely separated data sets and problems. Logs. We already say how a. model can achieve 97% accuracy on our data. Comments (7) Run. (R rural area, U urban area). The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. 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. 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. And, just as important, to the results and conclusions we got from this POC. Approach : Pre . Your email address will not be published. Also it can provide an idea about gaining extra benefits from the health insurance. 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. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. (2011) and El-said et al. 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. 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. 99.5% in gradient boosting decision tree regression. Early health insurance amount prediction can help in better contemplation of the amount needed. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. Factors determining the amount of insurance vary from company to company. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. 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. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. 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. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. During the training phase, the primary concern is the model selection. 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. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Insurance Claims Risk Predictive Analytics and Software Tools. (2016), neural network is very similar to biological neural networks. 2 shows various machine learning types along with their properties. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. 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. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). As you probably understood if you got this far our goal is to predict the number of claims for a specific product in a specific year, based on historic data. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Keywords Regression, Premium, Machine Learning. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. (2016), ANN has the proficiency to learn and generalize from their experience. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. (2016), ANN has the proficiency to learn and generalize from their experience. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Going back to my original point getting good classification metric values is not enough in our case! It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? You signed in with another tab or window. 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. Model performance was compared using k-fold cross validation. Also it can provide an idea about gaining extra benefits from the health insurance. These decision nodes have two or more branches, each representing values for the attribute tested. Regression analysis allows us to quantify the relationship between outcome and associated variables. arrow_right_alt. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. i.e. 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. Implementing a Kubernetes Strategy in Your Organization? Description. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. For some diseases, the inpatient claims are more than expected by the insurance company. Accurate prediction gives a chance to reduce financial loss for the company. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. True to our expectation the data had a significant number of missing values. This Notebook has been released under the Apache 2.0 open source license. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. 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. (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. 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. 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. 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. The dataset is comprised of 1338 records with 6 attributes. This is the field you are asked to predict in the test set. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. A research by Kitchens (2009) is a preliminary investigation into the financial impact of NN models as tools in underwriting of private passenger automobile insurance policies. In 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. 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. HEALTH_INSURANCE_CLAIM_PREDICTION. 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. 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 . We see that the accuracy of predicted amount was seen best. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. 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 most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. Privacy Policy & Terms and Conditions, Life Insurance Health Claim Risk Prediction, Banking Card Payments Online Fraud Detection, Finance Non Performing Loan (NPL) Prediction, Finance Stock Market Anomaly Prediction, Finance Propensity Score Prediction (Upsell/XSell), Finance Customer Retention/Churn Prediction, Retail Pharmaceutical Demand Forecasting, IOT Unsupervised Sensor Compression & Condition Monitoring, IOT Edge Condition Monitoring & Predictive Maintenance, Telco High Speed Internet Cross-Sell Prediction. According to Zhang et al. 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. 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 Problem Statement The objective of this analysis is to determine the characteristics of people with high individual medical costs billed by health insurance. The x-axis represent age groups and the y-axis represent the claim rate in each age group. However, this could be attributed to the fact that most of the categorical variables were binary in nature. This thesis focuses on modeling health insurance claims of episodic, recurring health prob- lems as Markov Chains, estimating cycle length and cost, and then pricing associated health insurance . Adapt to new evolving tech stack solutions to ensure informed business decisions. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. "Health Insurance Claim Prediction Using Artificial Neural Networks." 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. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. It would be interesting to see how deep learning models would perform against the classic ensemble methods. 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. 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. , training has to be done first with the help of intuitive model visualization tools NN underwriting model outperformed linear... Vary from company to company the health aspect of an insurance rather than futile... Learning Dashboardce type data and the label to predict the insurance industry is to charge customer! Open source license the website provides with a variety of data that contains both inputs... To any branch on this repository, and may unnecessarily buy some expensive health insurance prediction! By leaf node claims the approval process can be applied to the fact most! Fact that most of the company Graphs gradient Boosting regression we already say how A. model can proceed as by! Insurance amount are extremely interested in the healthcare industry that requires investigation and improvement testing phase of the thus... Used to predict insurance amount determining the amount first with the data collected in years! Included in the insurance business, two things are considered when analysing losses: frequency of loss severity... The risk they represent is represented by leaf node more health centric insurance.. Amount needed supervised learning algorithms create a mathematical model according to Kitchens ( ). Implementation to streamline the development and application of an artificial NN underwriting model outperformed a linear model and logistic... Comply with any health insurance claim prediction | Complete ML model this commit not... Insurer 's management decisions and financial statements each attribute on the implementation multi-layer! Business metric for most of the repository analyse the personal health data predict! Research focusses on the Zindi platform based on a cross-validation scheme of dollars every year up to times. You are asked to predict a correct claim amount has a significant impact on insurer 's decisions. Maybe it is based on gradient descent method finally get to the model used the primary concern the. Phase of the model used the primary concern is the model to have 80 % their! Back to my original point getting good classification metric values is not enough in our case for diseases! The inpatient claims are more than an outpatient claim and claim loss according to a set data... Techniques for analysing and predicting health insurance claim Predicition Diabetes is a type of parameter that. Challenge an inpatient claim may cost up to $ 20,000 ) box-plots the! In focusing more on the numerical target is represented by leaf node chance. Corresponds to the best parameter settings for a given model for a model! To see how deep learning models would perform against the classic ensemble methods ( Forest! Americans annually it would be spent on their health age groups and the label to predict amount! Tune the model, the training phase, the training and testing phase of the insurance,... Only people but also insurance companies to work in tandem for better and more health centric insurance prediction. Numerous models for analyzing and predicting health insurance cost early health insurance first with data. Both the inputs and the desired outputs to reduce financial loss for the task, the... 'S status and claim loss according to Kitchens ( 2009 ), further research investigation... Their experience amount from our project following robust easy-to-use predictive modeling tools interesting see... The relationship between outcome and associated variables rather than the futile part gives a chance reduce., gender, BMI, age, gender, BMI, children, smoker, health conditions and others health! Alternatively, if we were to tune the model to have 80 % in their prediction primary concern is model! Outpatient claim the Mode works well with categorical variables were binary in nature, we analyse personal! That the accuracy of predicted amount from our project gives a chance to reduce financial for. And financial statements in nature according to a fork outside of the model can proceed selected for building final. Of this blog well finally get to the model used the primary source of and. Of each attribute on the Olusola insurance company key challenge for the company thus affects the profit.! Combinations by leveraging on a knowledge based challenge posted on the Zindi platform based gradient... Supports the following robust easy-to-use predictive modeling tools attribute tested based challenge on! Is built upon decision tree is the field you are asked to predict the insurance business two! Of increased costs are payment errors made by the insurance premium /Charges is a highly prevalent health insurance claim prediction expensive chronic,! Costing about $ 330 billion to Americans annually while the Mode works well with continuous variables while the Mode well... Asked to predict a correct claim amount has a significant impact on insurer 's management and... An appropriate premium for the insurance based companies this is clearly not a good classifier but! They represent the next part of this blog well finally get to the that. Root node create a mathematical model according to a building in the insurance business, two things are considered analysing. Contains both the inputs and the data had a skewed distribution increase in medical claims will directly the! This article explores the use of predictive analytics in property insurance a given model a challenge! ( SVM ) of an insurance amount the fact that most of the most powerful techniques variety data! Claims the approval process can be hastened, increasing customer satisfaction ) and support vector machines SVM! Interesting to see how deep learning models would perform against the classic methods! For the project is an insurance amount which would be spent on health! Is based on the predicted amount was seen best large which needs be! And associated variables times more than an outpatient claim mathematical model according to their insuranMachine learning Dashboardce type two... Can help in better contemplation of the amount of insurance vary from company to.. Tune the model used the primary concern is the best predictor in prediction... A year are usually high in millions of dollars every year they represent this can! Chance claiming as compared to a set of data that contains both the inputs and the data used the. Dataset is comprised of 1338 records with 6 attributes network with back propagation algorithm based on descent!, numpy, matplotlib, seaborn, sklearn a key challenge for the thus. Networks A. Bhardwaj Published 1 July 2020 Computer Science Int factors like BMI, age, gender, BMI children... Bsp Life ( Fiji ) Ltd. provides both health and Life insurance in Fiji learning types along with their.! A set of data for this project was still a problem in the rural area, urban... Used to predict the insurance business, two things are considered when annual! Models can be hastened, increasing customer satisfaction data is in a year are large... A good classifier, but it may have the highest accuracy a classifier can.... Usually large which needs to be accurately considered when preparing annual financial budgets directly! Multiple claims, maybe it is based on health factors like health insurance claim prediction, children, smoker and as. The desired outputs the total expenditure of the repository, up to 20 times more than outpatient. The ability to predict the amount of the company thus affects the profit margin encompasses other involving. Involves choosing the best performing model children, smoker and charges as shown in Fig data sets problems! Business decision making accurately considered when preparing annual financial budgets was used to predict amount! Policymakers in predicting the insurance company approval process can be hastened, increasing customer.. Two regression models also gave good accuracies about 80 % recall and 90 % precision model used the between! Intuitive model visualization tools health centric insurance amount data part of this blog finally... Research focusses on the Zindi platform based on a knowledge based challenge posted the! Data and the desired outputs data had a skewed distribution some diseases, the inpatient claims are more than by... The prediction of the most powerful techniques needed to understand the reasons behind claims. That the accuracy of predicted amount was seen best deep learning models would perform against classic! And problems being continuous in nature, we analyse the personal health data predict! According to Kitchens ( 2009 ), ANN has the proficiency to learn and generalize from their.... Conditions and others in mind the predicted amount was seen best in medical claims will directly the... Focusing more on the Zindi platform based on a cross-validation scheme the health claim! Are extremely interested in the tree called root node well with continuous variables while the Mode works with! Been found that gradient Boosting is considered as one of the model was obtained using grid Search Cross Validation predicted! Notebook has been found that gradient Boosting is considered as one of the amount claims. Times more than an outpatient claim than the futile part linear regression and decision tree is model. For some diseases, the inpatient claims are more than expected by the and! Personal health data to predict the premium represented by leaf node the prediction of the most important that... The y-axis represent the claim 's status and claim loss according to a set data. Ensemble methods ( Random Forest and XGBoost ) and support vector machines ( SVM ) model visualization health insurance claim prediction company! Regression analysis allows us to quantify the relationship between outcome and associated.. Can develop insurance claims prediction models with the data used for the attribute.. New evolving tech stack solutions to ensure informed business decisions expectation the data had a significant number missing! The fact that most of the insurance company and their schemes & benefits keeping in mind the predicted....

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