Predictive Analytics In Telemonitoring: Early Detection Of High-Risk Patients Using Machine Learning

Abstract

The increasing burden of chronic diseases, ageing populations, and constraints on traditional healthcare systems have accelerated interest in telemonitoring as a strategic tool for early risk identification, clinical decision support, and personalised care delivery. Predictive analytics, driven by machine learning (ML), has emerged as a transformative enabler in telemonitoring ecosystems by enabling continuous analysis of physiological signals, behavioural trends, and environmental data to forecast high-risk events before clinical deterioration occurs. This study investigates the integration of ML-driven predictive analytics within telemonitoring systems, focusing on the early detection of high-risk patients across chronic illnesses such as cardiovascular disease, diabetes, respiratory disorders, and geriatric frailty. A mixed-methods research design incorporating quantitative datasets, qualitative clinician insights, and a detailed case study of a real-world telemonitoring deployment was utilised. Findings reveal that ML algorithms—particularly Random Forest, XGBoost, LSTM, and hybrid deep learning architectures—significantly enhance predictive accuracy for adverse events, often identifying deterioration 24–72 hours before manifestation. The study further explores system architecture, ethical dimensions, user adoption, data interoperability challenges, and the implications for health policy and digital transformation. Outcomes highlight the opportunity for telemonitoring to shift healthcare from reactive models to proactive, preventative, and data-driven frameworks, provided that regulatory clarity, data governance, infrastructural readiness, and patient engagement are strengthened.  The research offers evidence-based recommendations for scalable, equitable, and clinically reliable machine-learning-enabled telemonitoring systems