Al Bataineh Ali, Manacek Sarah
Department of Electrical and Computer Engineering, Norwich University, Barre, VT 05663, USA.
Department of Nursing, College of Nursing and Health Sciences, The University of Vermont, Burlington, VT 05405, USA.
J Pers Med. 2022 Jul 25;12(8):1208. doi: 10.3390/jpm12081208.
Machine Learning (ML) is becoming increasingly popular in healthcare, particularly for improving the timing and accuracy of diagnosis. ML can provide disease prediction by analyzing vast amounts of healthcare data, thereby, empowering patients and healthcare providers with information to make informed decisions about disease prevention. Due to the rising cost of treatment, one of the most important topics in clinical data analysis is the prediction and prevention of cardiovascular disease. It is difficult to manually calculate the chances of developing heart disease due to a myriad of contributing factors.
The aim of this paper is to develop and compare various intelligent systems built with ML algorithms for predicting whether a person is likely to develop heart disease using the publicly available Cleveland Heart Disease dataset. This paper describes an alternative multilayer perceptron (MLP) training technique that utilizes a particle swarm optimization (PSO) algorithm for heart disease detection.
The proposed MLP-PSO hybrid algorithm and ten different ML algorithms are used in this study to predict heart disease. Various classification metrics are used to evaluate the performance of the algorithms.
The proposed MLP-PSO outperforms all other algorithms, obtaining an accuracy of 84.61%.
According to our findings, the current MLP-PSO classifier enables practitioners to diagnose heart disease earlier, more accurately, and more effectively.
机器学习（ML）在医疗保健领域越来越受欢迎，特别是在提高诊断的及时性和准确性方面。机器学习可以通过分析大量医疗数据来提供疾病预测，从而为患者和医疗服务提供者提供信息，以便他们就疾病预防做出明智的决策。由于治疗成本不断上升，临床数据分析中最重要的主题之一是心血管疾病的预测和预防。由于众多因素的影响，手动计算患心脏病的几率很困难。
本文旨在开发并比较使用ML算法构建的各种智能系统，这些系统利用公开可用的克利夫兰心脏病数据集来预测一个人是否可能患心脏病。本文描述了一种替代的多层感知器（MLP）训练技术，该技术利用粒子群优化（PSO）算法进行心脏病检测。
本研究使用所提出的MLP-PSO混合算法和十种不同的ML算法来预测心脏病。使用各种分类指标来评估算法的性能。
所提出的MLP-PSO算法优于所有其他算法，准确率达到84.61%。
根据我们的研究结果，当前的MLP-PSO分类器使从业者能够更早、更准确、更有效地诊断心脏病。
