Abstract
One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.
Highlights
A variety of conditions that affect the normal working of the heart are known as heart diseases
The literature used in this study was selected on the basis of a particular criteria as given: (i) Only Coronary artery disease (CAD), heart failure (HF), cardio vascular disease (CVD), and chronic heart failure (CHF) are targeted in this study (ii) The articles published from 1995 to 2021 (iii) Those papers were considered that employed machine learning (ML) techniques for the diagnosis of the heart diseases (iv) The articles published in the English language are targeted in this study (v) Articles that used different types of data modalities like ECG, images, and clinical features for automated detection of heart diseases were considered (vi) The research articles that made use of publicly available datasets and electronic health records
This study provides the following key objects based on explicit analysis of the works that have been published in last 26 years: (i) The proposed ML techniques on the basis of the modality used, their benefits, and weaknesses (ii) The dataset properties according to modalities (iii) Performance measurement of the ML algorithms in terms of different evaluation metrics, namely, accuracy (ACC), specificity (Spec), and sensitivity (Sen)
Summary
A variety of conditions that affect the normal working of the heart are known as heart diseases. Referred to as cardio vascular disease (CVD), defines the condition where the blood vessels are narrowed or blocked leading to a heart attack (myocardial infarction) and chest pain (angina). Cardiovascular disease abrupts the normal working of the heart that pumps sufficient amount of blood in the human body, without boosting the intracardiac pressure. CHF is an expeditious healthcare problem [2] of the modern world, and 26 million adults around the globe are suffering from congestive heart failure [3]. A large amount of data on patients has been generated in the healthcare sector. The healthcare sector is facing major challenges in quality of service (QoS) which ensures correct and timely diagnosis of disease that results in competent treatment of the patients. Impaired diagnosis leads to detrimental results which are not acceptable [7]
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