Heart disease is one of the significant challenges in today's world and one of the leading causes of many deaths worldwide. Recent advancement of machine learning (ML) application demonstrates that using electrocardiogram (ECG) and patient data, detecting heart disease during the early stage is feasible. However, both ECG and patient data are often imbalanced, which ultimately raises a challenge for the traditional ML to perform unbiasedly. Over the years, several data level and algorithm level solutions have been exposed by many researchers and practitioners. To provide a broader view of the existing literature, this study takes a systematic literature review (SLR) approach to uncover the challenges associated with imbalanced data in heart diseases predictions. Before that, we conducted a meta-analysis using 451 referenced literature acquired from the reputed journals between 2012 and November 15, 2021. For in-depth analysis, 49 referenced literature has been considered and studied, taking into account the following factors: heart disease type, algorithms, applications, and solutions. Our SLR study revealed that the current approaches encounter various open problems/issues when dealing with imbalanced data, eventually hindering their practical applicability and functionality.
翻译:近来机器学习应用的进步表明,使用心电图(ECG)和病人数据,在早期发现心脏病是可行的,但是,ECG和病人数据往往不平衡,这最终对传统ML的公正表现提出了挑战。多年来,许多研究人员和从业者暴露了多种数据水平和算法水平解决方案。为了对现有文献进行更广泛的了解,本研究采取了系统化的文献审查(SLR)方法,以发现与心脏病预测中不平衡数据有关的挑战。在此之前,我们利用2012年至2021年11月15日期间从被统计的期刊上获取的451项参考文献进行了元分析。关于深入分析,参考了49项文献,并参考了以下因素:心脏病类型、算法、应用和解决方案。我们的SLR研究表明,在处理不平衡数据时,当前的方法遇到各种开放的问题/问题,最终阻碍了这些数据的实际适用性和功能。