Machine Learning Techniques in Adaptive and Personalized Systems for Health and Wellness

Traditional health systems mostly rely on rules created by experts to offer adaptive interventions patients. However, with recent advances in artificial intelligence (AI) and machine learning (ML) techniques, health-related are becoming more sophisticated higher accuracy providing personalized or treatments individual In this paper, we present an extensive literature review explore the current trends ML-based for well-being. We conduct a systematic search articles published between January 2011 April 2022 selected 87 that met our inclusion criteria review. The target 18 wellness domains including disease management, assistive healthcare, medical diagnosis, mental health, physical activity, dietary monitoring, substance use, smoking cessation, homeopathy remedy finding, patient privacy, mobile (mHealth) apps finder, clinician knowledge representation neonatal emergency care, dental oral medication surveillance, specialty recommendation, awareness. Our focuses five key areas across domains: data collection strategies, model development process, ML techniques utilized, evaluation as well personalization strategies interventions. also identified various technical methodological challenges volume constraints, quality issues, diversity variability infrastructure-related suitability of which directions future work area. Finally, recommendations tackling these challenges, leveraging technological such multimodality, Cloud technology, online learning, edge computing, automatic re-calibration, Bluetooth auto-reconnection, feedback pipeline, federated explainable AI, co-creation