A Survey of Cardiovascular Disease Identification and Prediction Using Machine Learning and Deep Learning Methods

The majority of mortality and morbidity in a variety of populations are caused by cardiovascular disease (CVD), resulting in a significant global health concern. The identification of disease through CT, MRI and radiology analysing through image screening to identify the exact region of blocks, strokes, plages or irregularity of vital signs, there is an increasing urgency to establish more effective identification and prediction methods that can facilitate timely interventions and improve patient outcomes. The application of modern technologies such as machine learning (ML) and deep learning (DL) has garnered considerable attention in the healthcare industry as a solution to the limitations of conventional diagnostic techniques. These data-driven techniques offer novel solutions for analysing complex medical data, enabling healthcare professionals to enhance diagnostic accuracy and predictive capabilities for cardiovascular conditions. This study aims to identify CVD by providing a comprehensive analysis of various ML and DL methods. Based on the datasets and performance indicators, techniques such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forests (RF), and Convolutional Neural Networks (CNN) are evaluated for training and validation. Furthermore, we investigate the emerging trends within this rapidly evolving field, including the role of big data, wearable technology, and real-time health monitoring systems. Significant progress has been produced, presenting challenges such as data privacy concerns, sample interpretation, and the necessity for robust clinical validation. As such, this paper also outlines future directions for research aimed at overcoming these obstacles while improving the integration of ML and DL into clinical practice. This survey contributes to a more profound experience of how ML and DL can revolutionize CVD’s early detection and management by delineating the current landscape and potential avenues for further exploration.