A comprehensive survey of artificial intelligence methods for cardiovascular disease detection: Recent advances and future challenges.

Global health is increasingly concerned with and interested in Cardiovascular Diseases (CVD), which necessitates new and innovative ways of identifying them earlier and treating them more effectively. AI techniques, such as ML and DL provide a promising pathway to address these challenges. This study investigated the interplay between advancing technology and medical science, focusing on AI's application in improving CVD diagnosis. Traditionally, CVD diagnosis has relied on clinical assessments, laboratory tests, and imaging modalities such as echocardiography and angiography. Several researchers are using online datasets as well as utilizing inexpensive sensors to collect data in the healthcare field to carry out their research to develop different ML and DL algorithms that can detect diseases automatically. Feature-based ML algorithms, CNNs, RNNs, and hybrid models are commonly used techniques in this area. This study highlights the importance of ML and DL in cardiac health and emphasizes precise and enhanced prediction of cardiovascular disease. The developments in state-of-art technologies and the increasing influence of cardiovascular disease on public health, this study attempts to present an in-depth analysis of the topic based on current AI-based methods used for CVD management based on reports from Electronic Health Records (EHR) and Electrocardiogram (ECG). It shows areas which requires improvement, and proposes avenues for future investigation. This study aims to direct future advancements in diagnostic tools by highlighting the critical role of AI in rethinking methods to CVD diagnosis and treatment approaches to enhance the patient outcomes.