基于软集的参数缩减算法--通过可辨别性矩阵和混合方法,利用各种机器学习技术预测心血管疾病的风险因子

IF 0.6 Q3 MULTIDISCIPLINARY SCIENCES Pertanika Journal of Science and Technology Pub Date : 2023-11-24 DOI:10.47836/pjst.32.1.16
Menaga Anbumani, Kannan Kaniyaiah
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引用次数: 0

摘要

在决策问题中,减少参数而不降低性能是一项大有可为的任务。例如,在博弈论中构建成本函数就是一个巨大的挑战。不过,软集合理论通过一种新的选择函数数学工具,可以方便地处理所有缺点。在本文中,我们提出了一种通过可辨矩阵减少软集参数的算法(SSPRDM),并利用六种机器学习技术将其用于检测心脏病问题的风险因素。通过 SSPRDM 算法从心脏病患者数据中提取参数,然后使用六种机器学习技术(LDA、KNN、SVM、CART、NB、RF)预测心脏病风险因素。实验结果表明,本混合方法在随机森林技术中的准确率为 88.46%,而在早期工作中,同样的随机森林分类器在心血管疾病(CVD)诊断风险因素预测中的准确率为 69.23%,这是一个巨大的进步。此外,在 18 个参数还原中,核心成分被确定为总胆固醇,这在所有类型的心血管疾病诊断中都要考虑,而空腹糖(C)、总胆固醇(G)和高密度脂蛋白胆固醇(I)是 ABCEGHI、ACFGIJ 和 BCFGIJK 三个参数还原中确定的核心成分。
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Soft Set-based Parameter Reduction Algorithm Through a Discernibility Matrix and the Hybrid Approach for the Risk-Factor Prediction of Cardiovascular Diseases by Various Machine Learning Techniques
Parameter reduction without performance degradation is a promising task in decision-making problems. For instance, a great challenge exists in constructing cost functions in gaming theory. Nevertheless, soft set theory handles all its drawbacks conveniently through a new tool for the choice function mathematically. In this paper, we propose an algorithm (SSPRDM) for parameter reduction of soft sets through discernibility matrices, and it is implemented in detecting the risk factor of heart disease problems by using six types of machine learning techniques. The parameters are extracted from the heart disease patient data by the SSPRDM algorithm, and then six machine learning techniques (LDA, KNN, SVM, CART, NB, RF) are performed in the prediction of risk factors for heart disease. The experimental results showed that the present hybrid approach provides an accuracy of 88.46% in the Random Forest technique, whereas the same Random Forest classifier provides an accuracy of 69.23% in the prediction of risk factors of cardiovascular disease (CVD) diagnosis in the earlier work which is a drastic improvement. Moreover, out of 18 parameter reductions, the core component is identified as Total Cholesterol, which is to be considered in all types of CVD diagnosis, whereas Sugar-Fasting (C), Total-Cholesterol (G), and HDL-Cholesterol (I) are the core components identified in three parameter reductions ABCEGHI, ACFGIJ, and BCFGIJK.
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来源期刊
Pertanika Journal of Science and Technology
Pertanika Journal of Science and Technology MULTIDISCIPLINARY SCIENCES-
CiteScore
1.50
自引率
16.70%
发文量
178
期刊介绍: Pertanika Journal of Science and Technology aims to provide a forum for high quality research related to science and engineering research. Areas relevant to the scope of the journal include: bioinformatics, bioscience, biotechnology and bio-molecular sciences, chemistry, computer science, ecology, engineering, engineering design, environmental control and management, mathematics and statistics, medicine and health sciences, nanotechnology, physics, safety and emergency management, and related fields of study.
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