Machine learning algorithms demonstrated significant potential in improving early detection and diagnostic accuracy across electrocardiogram, diabetes, chronic kidney disease, and breast cancer.
Systematic Review
Machine learning algorithms show transformative potential in disease detection and management across ECG, diabetes, CKD, and breast cancer, though challenges in data quality and model interpretability remain.
Abstract Purpose This paper comprehensively reviews the effects of machine learning (ML) on the diagnosis and treatment of four different diseases: electrocardiogram (ECG), diabetes, chronic kidney disease (CKD), and breast cancer. The primary objective of this paper is to investigate how ML algorithms address challenges related to early detection, diagnostic precision, and data security across various medical fields. Method The study employs a systematic review approach, formulating research questions and extensively analyzing existing literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology is utilized in the literature on these four diseases. This analysis focused on ML applications in performance metrics, disease prediction, and advancements in the field. The review integrates bibliometrics, PRISMA-based study selection, and an ML technique-based in-depth comparative review. Results The review indicates the transformative impact of ML on disease detection and management. Breast cancer research represents the most significant growth and citation rates, indicating its high relevance. CKD and diabetes research also demonstrated notable advancements but faced recent declines in output. However, electrocardiogram research requires further innovation with advancements in other areas. Conclusion ML algorithms have great potential to solve data security, early detection capabilities, and diagnostic accuracy problems, and provide new solutions in various medical fields. Future research should focus on improving healthcare delivery and patient outcomes by enhancing model robustness and integrating emerging technologies.
Kaur et al. (Thu,) conducted a systematic review in Electrocardiogram abnormalities, Diabetes, Chronic Kidney Disease, and Breast Cancer. Machine Learning Techniques was evaluated. Machine learning algorithms demonstrated significant potential in improving early detection and diagnostic accuracy across electrocardiogram, diabetes, chronic kidney disease, and breast cancer.