Manuscript Topics

This special issue offers a thorough analysis of the integration of advanced machine learning methods with the crucial domain of healthcare. Machine learning, a resilient subfield of artificial intelligence, has emerged as a valuable tool for unraveling complex patterns in medical data. This advancement has novel prospects for the anticipation and mitigation of illnesses. This compilation examines the intricate connection between cutting-edge machine learning methods and the pressing requirement to forecast, mitigate, and anticipate diseases at a crucial phase of technological progress. This special issue aims to define the limits of a forthcoming era in healthcare characterized by a shift in approach driven by data-driven insights. By analyzing existing research, methodology, and applications, it seeks to explore the potential for proactive and personalized illness management. We invite submissions of original research articles, reviews, and perspectives that investigate the application of machine learning algorithms in disease prediction, early detection, risk assessment, and tailored preventive interventions. We strongly appreciate contributions that include clear explanations of new methodology, data-driven approaches, clinical applications, and ethical considerations.

Keywords: early detection; predictive modelling; risk assessment; genomics; real-time monitoring; multi-omics data; medical imaging; machine learning; deep learning; bioinformatics

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