Healthcare Cost Prediction Based on Hybrid Machine Learning Algorithms

被引:3
|
作者
Zou, Shujie [1 ]
Chu, Chiawei [1 ]
Shen, Ning [2 ]
Ren, Jia [3 ]
机构
[1] City Univ Macau, Fac Data Sci, Macau 999078, Peoples R China
[2] United Arab Emirates Univ, Dept Innovat Technol & Entrepreneurship, POB 15551, Al Ain, U Arab Emirates
[3] Hainan Univ, Sch Informat & Commun Engn, Haikou 570100, Peoples R China
关键词
healthcare costs; CGBN; regression algorithm; hybrid algorithm; BAYESIAN NETWORK; MODELS;
D O I
10.3390/math11234778
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Healthcare cost is an issue of concern right now. While many complex machine learning algorithms have been proposed to analyze healthcare cost and address the shortcomings of linear regression and reliance on expert analyses, these algorithms do not take into account whether each characteristic variable contained in the healthcare data has a positive effect on predicting healthcare cost. This paper uses hybrid machine learning algorithms to predict healthcare cost. First, network structure learning algorithms (a score-based algorithm, constraint-based algorithm, and hybrid algorithm) for a Conditional Gaussian Bayesian Network (CGBN) are used to learn the isolated characteristic variables in healthcare data without changing the data properties (i.e., discrete or continuous). Then, the isolated characteristic variables are removed from the original data and the remaining data used to train regression algorithms. Two public healthcare datasets are used to test the performance of the proposed hybrid machine learning algorithm model. Experiments show that when compared to popular single machine learning algorithms (Long Short Term Memory, Random Forest, etc.) the proposed scheme can obtain similar or higher prediction accuracy with a reduced amount of data.
引用
收藏
页数:13
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