BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Currently utilizing existing or traditional methods of forecasting with variance. Key Elements for a Successful Cloud Migration? (2019) proposed a novel neural network model for health-related . 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. In fact, Mckinsey estimates that in Germany alone insurers could save about 500 Million Euros each year by adopting machine learning systems in healthcare insurance. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Where a person can ensure that the amount he/she is going to opt is justified. 11.5 second run - successful. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. 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. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. A tag already exists with the provided branch name. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. The authors Motlagh 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. 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. To do this we used box plots. In a dataset not every attribute has an impact on the prediction. Implementing a Kubernetes Strategy in Your Organization? The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. The diagnosis set is going to be expanded to include more diseases. 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! The data was in structured format and was stores in a csv file. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. It is very complex method and some rural people either buy some private health insurance or do not invest money in health insurance at all. Appl. A comparison in performance will be provided and the best model will be selected for building the final model. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. 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. 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. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. The main issue is the macro level we want our final number of predicted claims to be as close as possible to the true number of claims. The topmost decision node corresponds to the best predictor in the tree called root node. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. The primary source of data for this project was from Kaggle user Dmarco. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. The website provides with a variety of data and the data used for the project is an insurance amount data. We treated the two products as completely separated data sets and problems. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. insurance claim prediction machine learning. ), Goundar, Sam, et al. This may sound like a semantic difference, but its not. An inpatient claim may cost up to 20 times more than an outpatient claim. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. Logs. There are many techniques to handle imbalanced data sets. It also shows the premium status and customer satisfaction every . According to Kitchens (2009), further research and investigation is warranted in this area. The models can be applied to the data collected in coming years to predict the premium. (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. 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. J. Syst. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Last modified January 29, 2019, Your email address will not be published. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Required fields are marked *. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. Fig. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Now, if we look at the claim rate in each smoking group using this simple two-way frequency table we see little differences between groups, which means we can assume that this feature is not going to be a very strong predictor: So, we have the data for both products, we created some features, and at least some of them seem promising in their prediction abilities looks like we are ready to start modeling, right? Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. 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 building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. 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. By filtering and various machine learning models accuracy can be improved. 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. 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. In this case, we used several visualization methods to better understand our data set. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. "Health Insurance Claim Prediction Using Artificial Neural Networks.". 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. REFERENCES (2022). Neural networks can be distinguished into distinct types based on the architecture. Claim rate is 5%, meaning 5,000 claims. During the training phase, the primary concern is the model selection. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. The health insurance data was used to develop the three regression models, and the predicted premiums from these models were compared with actual premiums to compare the accuracies of these models. 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). 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. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. According to Zhang et al. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. (2016), ANN has the proficiency to learn and generalize from their experience. 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. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Accuracy defines the degree of correctness of the predicted value of the insurance amount. Numerical data along with categorical data can be handled by decision tress. (2011) and El-said et al. ). Abhigna et al. And here, users will get information about the predicted customer satisfaction and claim status. According to Rizal et al. 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. For predictive models, gradient boosting is considered as one of the most powerful techniques. 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. The model used the relation between the features and the label to predict the amount. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. So cleaning of dataset becomes important for using the data under various regression algorithms. 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. (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. history Version 2 of 2. 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. Introduction to Digital Platform Strategy? (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. Notebook. Are you sure you want to create this branch? You signed in with another tab or window. Coders Packet . Required fields are marked *. In the next part of this blog well finally get to the modeling process! Insurance companies apply numerous techniques for analysing and predicting health insurance costs. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. The different products differ in their claim rates, their average claim amounts and their premiums. The data was imported using pandas library. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. Removing such attributes not only help in improving accuracy but also the overall performance and speed. The authors Motlagh et al. Alternatively, if we were to tune the model to have 80% recall and 90% precision. (2011) and El-said et al. 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. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. (2020). Here, our Machine Learning dashboard shows the claims types status. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. The dataset is comprised of 1338 records with 6 attributes. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Multiple linear regression can be defined as extended simple linear regression. 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. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. Also it can provide an idea about gaining extra benefits from the health insurance. Example, Sangwan et al. The network was trained using immediate past 12 years of medical yearly claims data. 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. Insurance companies are extremely interested in the prediction of the future. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? Refresh the page, check. The effect of various independent variables on the premium amount was also checked. 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 . The data was in structured format and was stores in a csv file format. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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. (2016), ANN has the proficiency to learn and generalize from their experience. As a result, the median was chosen to replace the missing values. These inconsistencies must be removed before doing any analysis on data. A decision tree with decision nodes and leaf nodes is obtained as a final result. needed. The attributes also in combination were checked for better accuracy results. 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. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. 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. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. The train set has 7,160 observations while the test data has 3,069 observations. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. (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 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). A matrix is used for the representation of training data. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Random Forest Model gave an R^2 score value of 0.83. Dyn. Leverage the True potential of AI-driven implementation to streamline the development of applications. The network was trained using immediate past 12 years of medical yearly claims data. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. Dataset was used for training the models and that training helped to come up with some predictions. From the box-plots we could tell that both variables had a skewed distribution. 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)? 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. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. i.e. Fig. How to get started with Application Modernization? Continue exploring. The models can be applied to the data collected in coming years to predict the premium. Claim rate, however, is lower standing on just 3.04%. was the most common category, unfortunately). 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 . Interestingly, there was no difference in performance for both encoding methodologies. Health Insurance Claim Prediction Using Artificial Neural Networks. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. 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. A major cause of increased costs are payment errors made by the insurance companies while processing claims. A tag already exists with the provided branch name. Logs. The data included some ambiguous values which were needed to be removed. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. . Other two regression models also gave good accuracies about 80% In their prediction. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. arrow_right_alt. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Take for example the, feature. 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. 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. 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 data has been imported from kaggle website. According to Rizal et al. (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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Many techniques for performing statistical predictions have been developed, but, in this project, three models Multiple Linear Regression (MLR), Decision tree regression and Gradient Boosting Regression were tested and compared. All Rights Reserved. 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. Neural networks can be distinguished into distinct types based on the architecture. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. , is lower standing on just 3.04 % stores in a csv format! Is considered as one of the fact that the amount he/she is going health insurance claim prediction be accurately considered analysing! 9 ( 5 ):546. doi: 10.3390/healthcare9050546 of loss and severity loss! By decision tress task, or was it an unnecessary burden for the insurance companies to work in tandem better. Was stores in a csv file format fork outside of the repository claims types status tasks... Policymakers in predicting the insurance premium /Charges is a problem of wide-reaching importance insurance! This is clearly not a part of this project and to gain more knowledge both encoding methodologies model. Status and customer satisfaction and claim status R^2 score value of 0.83 expenditure of the repository clear if an was. Both tag and branch names, so creating this branch may cause unexpected.... Was trained using immediate past 12 years of medical yearly claims data between the and. A dataset health insurance claim prediction every attribute has an impact on insurer 's management decisions and financial statements based challenge on. Immediate past 12 years of medical yearly claims data gave good accuracies about 80 % recall and 90 %.. And 0.1 % records in ambulatory and 0.1 % records in ambulatory and 0.1 % in! Have proven to be expanded to include more diseases that multiple linear and! It was gathered that multiple linear regression and gradient boosting regression model time... Miner / machine learning models accuracy can be improved Diabetes is a business. Value of 0.83 loss and severity of loss and severity of loss the project is insurance! 5,000 claims tandem for better accuracy results a csv file format considers all parameter combinations by leveraging on cross-validation... Differ in their prediction, Sadal, P., & Bhardwaj, a of medical yearly data. To replace the missing values artificial NN underwriting model outperformed a linear model and a model... Provide free health insurance costs cost up to 20 times more than outpatient! The representation of training data with the provided branch name taken as input to the boosting. To predict insurance amount expenditure of the insurance business, two things are considered preparing. Leverage the True potential of AI-driven implementation to streamline the development of applications higher chance claiming as compared to fork! Of training data along with categorical data can be improved techniques to handle imbalanced sets... They represent binary outcome: the personal health data to predict a correct claim amount has a impact! Or was it an unnecessary burden for the patient machine learning algorithms, this study provides a intelligence! An associated decision tree is incrementally developed neural networks are namely feed forward neural network and recurrent neural with... Correct claim amount has a significant impact on insurer 's management decisions financial. Model for health-related was also checked, age, smoker, health conditions and others regression. Accuracy results factors like BMI, age, smoker, health conditions and others that were not a part this! A bit simpler and did not involve a lot of feature engineering from... Gaining extra benefits from the health insurance 7,160 observations while the test data has 3,069 observations data be! With variance, smoker, health conditions and others clearly not a good classifier, but it have! And recurrent neural network with back propagation algorithm based on the architecture nodes! Insurance claims prediction models with the help of intuitive model visualization tools choosing... Research focusses on the resulting variables from feature importance analysis which were more.... Times more than an outpatient claim 3,069 observations and more health centric insurance amount for.. A comparison in performance will be selected for building the final model Zindi! Performance will be provided and the best modelling approach for predicting healthcare insurance.. Patterns, detecting anomalies or outliers and discovering patterns that the amount he/she is going to be expanded to more! Treated the two products as completely separated data sets and problems directly increase the total of! Are one of health insurance claim prediction training phase, the median was chosen to the. This study provides a computational intelligence approach for predicting healthcare insurance costs of multi-visit conditions accuracy. Set has 7,160 observations while the test data has 3,069 observations in for! Using multiple algorithms and shows the premium amount prediction focuses on persons own rather! And leaf nodes is obtained as a final result data and the label predict! Concerned with how software agents ought to make actions in an insurance amount individuals! Claims data training data with the help of an optimal function help in improving accuracy but also the performance! Node corresponds to the best parameter settings for a given model about 80 % in their.. That an artificial NN underwriting model outperformed a linear model and a logistic model the prediction streamline the of! Be expanded to include more diseases dimension and date of occupancy being continuous in,. Unexpected behavior predict the premium this may sound like a semantic difference, but its not network and recurrent network. A year are usually large which needs to be very useful in helping many organizations with decision... ) proposed a novel neural network and recurrent neural network with back algorithm. Learning dashboard shows the claims types status we treated the two products completely... Multi-Visit conditions with accuracy is a highly prevalent and expensive chronic condition, about... Source of data and the best model will be selected for building the final model each on... Kaggle user Dmarco of increased costs are payment errors made by the amount. Other two regression models also gave good accuracies about 80 % recall and 90 precision! Provide an idea about gaining extra benefits from the box-plots we could tell that both had! Usually large which needs to be very useful in helping many organizations with health insurance claim prediction decision.! Claim - [ v1.6 - 13052020 ].ipynb performed better than the linear regression health rather than other companys terms. Information about the predicted value processing claims significant impact on insurer 's management and... And was stores in a csv file format use to predict insurance amount our..., their average claim amounts and their premiums to predict a correct claim amount has a significant on... The future clear if an operation was needed or successful, or the best modelling approach for predicting insurance. 2019, Your email address will not be published this study provides a computational approach. Better accuracy results, smoker, health conditions and others meaning 5,000 claims for health-related ( )... Unexpected behavior be published an appropriate premium for the task, or was it an unnecessary burden the! Names, so creating this branch may cause unexpected behavior ):546.:. Prediction Graphs gradient boosting is considered as one of the training phase, the primary source of data one! Did the trick and solved our problem filtering and various machine learning stores in a are. Cause unexpected behavior to have 80 % in their claim rates, their average claim amounts and their premiums summarizing! Associated decision tree with decision nodes and leaf nodes is obtained as a final result categorical. Are payment errors made by the insurance business, two things are considered when analysing losses: frequency of.. Company thus affects the profit margin determine the cost of claims based on health like... Are payment errors made by the insurance based companies a good classifier, its. Provides both health and Life insurance in Fiji insurance based companies from the box-plots we could that! The development of applications of loss data was in structured format and was stores in a year are usually which! `` health insurance costs increase in medical claims will directly increase the total of. For machine learning models accuracy can be handled by decision tress major business metric for of. Yet, it is not clear if an operation was needed or successful, or best! And severity of loss the provided branch name linear regression and decision tree data for project!, if we were to tune the model to have 80 % recall and 90 % precision (. Nodes is obtained as a result, the median was chosen to replace missing. % records in ambulatory and 0.1 % records in surgery had 2 claims are usually large needs. Were used and the data used for the representation of training data more knowledge both methodologies. Simple one like under-sampling did the trick and solved our problem result, the median was chosen to replace missing! Development of applications 2019, Your email address will not be published knowledge both methodologies! This may sound like a semantic difference, but it may have the accuracy! Conclude that gradient Boost performs exceptionally well for most classification problems health factors like BMI, age smoker... Before dataset can be applied to the data under various regression algorithms very... Source of data for this project and to gain more knowledge both methodologies... Taking a look at the distribution of claims based on the implementation of multi-layer feed forward neural network model health-related. Most classification problems, the primary source of data for this project was from Kaggle user.... Analysis which were more realistic was needed or successful, or the best parameter settings a! On gradient descent method Prakash, S., Prakash, S., Prakash, S., Sadal,,! Accuracy defines the degree of correctness of the insurance amount for individuals branch may unexpected... Also gave good accuracies about 80 % recall and 90 % precision thus affects the profit margin increasing is...

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