Ullah Farhat, Chen Xin, Rajab Khairan, Al Reshan Mana Saleh, Shaikh Asadullah, Hassan Muhammad Abul, Rizwan Muhammad, Davidekova Monika
School of Automation, China University of Geosciences, Wuhan 430074, China.
College of Computer Science and Information Systems Najran University, Najra 61441, Saudi Arabia.
Comput Intell Neurosci. 2022 Jul 13;2022:1906466. doi: 10.1155/2022/1906466. eCollection 2022.
Coronary heart disease has an intense impact on human life. Medical history-based diagnosis of heart disease has been practiced but deemed unreliable. Machine learning algorithms are more reliable and efficient in classifying, e.g., with or without cardiac disease. Heart disease detection must be precise and accurate to prevent human loss. However, previous research studies have several shortcomings, for example,take enough time to compute while other techniques are quick but not accurate. This research study is conducted to address the existing problem and to construct an accurate machine learning model for predicting heart disease. Our model is evaluated based on five feature selection algorithms and performance assessment matrix such as accuracy, precision, recall, F1-score, MCC, and time complexity parameters. The proposed work has been tested on all of the dataset'sfeatures as well as a subset of them. The reduction of features has an impact on theperformance of classifiers in terms of the evaluation matrix and execution time. Experimental results of the support vector machine, K-nearest neighbor, and logistic regression are 97.5%,95 %, and 93% (accuracy) with reduced computation timesof 4.4, 7.3, and 8seconds respectively.
冠心病对人类生活有着严重影响。基于病史对心脏病进行诊断的做法由来已久，但被认为不可靠。机器学习算法在心脏病分类方面，比如区分有无心脏病，更为可靠且高效。心脏病检测必须精确无误，以避免人员伤亡。然而，以往的研究存在若干缺陷，例如，有些算法计算耗时过长，而其他技术虽速度快但不够准确。开展本研究旨在解决现有问题，并构建一个用于预测心脏病的精确机器学习模型。我们的模型基于五种特征选择算法以及诸如准确率、精确率、召回率、F1分数、马修斯相关系数和时间复杂度参数等性能评估矩阵进行评估。所提出的工作在数据集的所有特征以及部分特征子集上进行了测试。特征约简在评估矩阵和执行时间方面对分类器的性能产生影响。支持向量机、K近邻和逻辑回归的实验结果准确率分别为97.5%、95%和93%，计算时间分别减少至4.4秒、7.3秒和8秒。
