A Review, Synthesizing Frameworks, and Future Research Agenda: Use of AI & ML Models in Cardiovascular Diseases Diagnosis

Abstract

Cardiovascular diseases (CVDs) continue to be a leading cause of morbidity and mortality worldwide. Early detection and accurate diagnosis of the initial phases of CVDs are crucial for effective intervention and improved patient outcomes. In recent years, advances in intelligent automation and machine learning (ML) techniques have shown promise in enhancing the accuracy and efficiency of CVD detection. This systematic review aims to comprehensively analyze and synthesize the existing literature on the application of intelligent automation and ML adaptive classifier models in the detection of the initial phase of cardiovascular disease within the realm of medical science. The review follows a rigorous systematic methodology, including comprehensive literature search, study selection, data extraction, and quality assessment. A wide range of scholarly articles from the reputed journal were searched to identify relevant studies published over a specified period. The selected studies were critically evaluated for methodological robustness and relevance to the research objective. The synthesis of findings reveals a diverse landscape of research endeavors focused on employing intelligent automation and ML adaptive classifier models for CVD detection. The review highlights the various types of ML algorithms utilized, such as neural networks, decision trees, and support vector machines, and their potential to enhance the accuracy of diagnosis by analyzing complex and heterogeneous data sources, clinical records, and omics data. Furthermore, the review discusses challenges and limitations encountered in implementing these models, including data quality, interpretability, and ethical considerations. It also underscores the importance of interdisciplinary collaboration between medical practitioners, data scientists, and domain experts to ensure the seamless integration of these innovative technologies into clinical practice. In conclusion, this systematic review underscores the significant advancements made in the field of intelligent automation and ML adaptive classifier models in the detection of the initial phase of cardiovascular disease. While acknowledging the potential of these approaches, it also emphasizes the need for further research, standardization, and validation to harness their full capabilities and contribute to more accurate, timely and personalized cardiovascular disease diagnosis and management.