  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.
