A medical price prediction system using hierarchical decision trees

In the domains of computational and applied mathematics, soft computing, fuzzy logic, and machine learning (ML) are well-known research areas. ML is one of the computational intelligence aspects that may address diverse difficulties in a wide range of applications and systems when it comes to exploitation of historical data. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. The proposed research approach uses Linear Regression, Support Vector Regression, Ridge Regressor, Stochastic Gradient Boosting, XGBoost, Decision Tree, Random Forest Regressor, Multiple Linear Regression, and k-Nearest Neighbors A medical insurance cost dataset is acquired from the KAGGLE repository for this purpose, and machine learning methods are used to show how different regression models can forecast insurance costs and to compare the models’ accuracy. The results shows that the Stochastic Gradient Boosting (SGB) model outperforms the others with a cross-validation value of 0.0.858 and RMSE value of 0.340 and gives 86% accuracy.

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“…In [24,25], hierarchical Decision Trees and other ML models are used for predictive analytics of healthcare costs.…”

Section: Related Workmentioning

confidence: 99%

A Computational Intelligence Approach for Predicting Medical Insurance Cost

Hassan

Iqbal

Hussain

et al. 2021

Mathematical Problems in Engineering

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“…Some of the work done in this project is done in the paper Tike et al [1]. The rest of the work in this report is organized in the following way.…”

Section: Fig 1 Organization Of This Projectmentioning

confidence: 99%

“…Among the various costs a hospital has to bear, a significant one relates to real estate costthe cost of owning a building or renting it. The real estate cost is a surrogate for other costs too in that if a certain region has high real estate costs, then it likely has higher costs in the other categories as well such as salary paid to doctors, staff etc [1]. Therefore, real estate cost will also be included in the feature set, in particular the median per-square foot price of homes -MedianPerSqFtPricewithin the hospital's ZIP code [32] compiled by the real estate portal www.zillow.com.…”

Section: Selection Of Features From the Datasetmentioning

confidence: 99%

“…Fig. 5 plots the real estate prices against the payment amounts for the DRG that has the highest number of prices for it, viz., "194 -SIMPLE PNEUMONIA & PLEURISY W CC" [1]. The linear trend line generated using the least-squares fit is shown.…”

Section: Selection Of Features From the Datasetmentioning

confidence: 99%

“…The relation between real estate and medical prices for the DRG: 194[1] 4.3 The Total Feature SetThe system uses following features to train and test the machine learning model.1) DRG Definition 2) Hospital Region Referral Description 3) Total Discharges 4) MedianPerSqFtPrice In the feature set, the DRG Definition and the Hospital Region Referral Description are categorical features where the Total Discharges and the MedianPerSqFtPrice are continuous features. While training the model, the Average Total Payment column becomes the "label" or the value being predicted by the model at the time of testing.…”

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confidence: 99%

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A Medical Price Prediction System

Tike

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The health care costs constitute a significant fraction of the U.S. economy. Nearly 20% of the Gross Domestic Product (GDP) is spent on health care. The health spending in the US is the highest among all developed nations in absolute numbers as well as a percentage of the economy. The U.S. government bears a large portion of seniors' health expenditure through its Medicare program. The growing health related expenses combined with the fact that the babyboomer generation is retiring, and hence they will be eligible for Medicare, puts a great burden on the U.S. exchequer. Therefore, it is essential to contain health related payments through all means possible. In this work, we will develop a medical price prediction system using machine learning algorithms which will aid in steering patients to cost effective providers and thereby curb health spending. The policymakers can also use the tool to better understand which providers are relatively expensive and take punitive actions if necessary. The prediction of the medical price will be done using implementing Random Forest Regression algorithm in machine learning. Additionally, we plan to include the experiments on the same data with other machine learning models such as Gradient Boosted Trees and Linear Regression and compare results. The findings from these experiments will also be included.

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