Optimizing Machine Learning Algorithms for Heart Disease Classification and Prediction

IF 1.7 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Abdeljalil El-Ibrahimi, Oumaima Terrada, Oussama El Gannour, Bouchaib Cherradi, Ahmed El Abbassi, Omar Bouattane
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引用次数: 0

Abstract

According to the World Health Organization (WHO), cardiovascular disease is one of the leading causes of death worldwide. Thus, the prevention of this kind of illness is considered as a huge human health challenge. Additionally, the diagnostic process often involves a combination of clinical examination, laboratory tests, and other diagnostic procedures, which can be complex and time-consuming. However, advances in medical technology and research have led to improved methods for diagnosing heart disease, which can help to improve patient outcomes. Furthermore, Machine Learning (ML) methods have shown promise in helping to improve the diagnosis of heart disease. Each method requires specific parameters to produce good results. In this paper, we propose a diagnosis support system based on optimized Machine Learning algorithms, which is Artificial Neural Network (ANN), Support Vector Machine (SVM), K_Nearest Neighbour (KNN), Naive Bayes (NB), and Decision Tree (DT) to analyze the major cardiovascular risk factors, such as age, gender, high blood pressure, etc. To train and validate the ML models, a medical dataset of 558 patients with atherosclerosis is used. In this work, we achieved a 96.67% as promising accuracy level for the atherosclerosis prediction with ANN.
优化心脏疾病分类和预测的机器学习算法
根据世界卫生组织(WHO)的数据,心血管疾病是世界范围内导致死亡的主要原因之一。因此,预防这类疾病被认为是一项巨大的人类健康挑战。此外,诊断过程通常包括临床检查、实验室检查和其他诊断程序,这可能是复杂和耗时的。然而,医疗技术和研究的进步导致了心脏病诊断方法的改进,这有助于改善患者的预后。此外,机器学习(ML)方法在帮助改善心脏病的诊断方面显示出了希望。每种方法都需要特定的参数才能产生良好的结果。本文提出了一种基于优化机器学习算法的诊断支持系统,该系统包括人工神经网络(ANN)、支持向量机(SVM)、K_Nearest Neighbour (KNN)、朴素贝叶斯(NB)和决策树(DT),用于分析年龄、性别、高血压等心血管疾病的主要危险因素。为了训练和验证ML模型,使用了558例动脉粥样硬化患者的医学数据集。在这项工作中,我们用人工神经网络预测动脉粥样硬化的准确率达到了96.67%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.00
自引率
46.20%
发文量
143
审稿时长
12 weeks
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