Applications of Machine Learning in Healthcare Diagnosis and Prognosis

Abstract

With its powerful data-driven capabilities, machine learning is revolutionizing healthcare by improving disease diagnosis and prognosis. There are new possibilities to improve clinical decision-making thanks to the proliferation of EHRs, medical imaging, genomic data, and real-time patient monitoring. Machine learning techniques are examined in this study for their potential use in healthcare diagnosis and prognosis, particularly in the areas of early disease detection, outcome prediction, and individualized therapy planning. frequently employed ML models, spanning supervised, unsupervised, and deep learning techniques, utilized in the fields of medical imaging, evaluation of clinical data, and risk classification. In particular, it looks at how these models help with timely interventions, decrease human error, and increase diagnostic accuracy. Problems include things like privacy worries, unintelligible models, poor data quality, and difficulties integrating into healthcare workflows. By combining clinical experience with machine learning-based solutions, diagnostic precision and prognosis dependability can be greatly improved. To guarantee safe and effective adoption in healthcare environments, machine learning solutions must be transparent, ethical, and clinically proven.