IoT-Based Early Detection of Chronic Diseases Using Machine Learning

Abstract

Chronic diseases, including cardiovascular disease, diabetes, chronic respiratory disease, and cancer, remain major causes of morbidity and premature mortality worldwide.The increasing availability of Internet of Things (IoT) devices, wearable sensors, smart phones, and remote monitoring platforms provides an opportunity to shift chronic disease management from periodic hospital-based assessment toward continuous, data-driven health monitoring.This seminar paper examines the application of IoT and machine learning (ML) for the early detection of chronic diseases.The paper reviews recent literature on IoT-enabled physiological sensing, wearable health technologies, machine learning algorithms, digital biomarkers, edge computing, explainable artificial intelligence, and privacy-preserving learning.Recent evidence shows that IoT devices can continuously collect physiological and behavioral indicators such as heart rate, blood pressure, blood oxygen saturation, glucose level, body temperature, sleep characteristics, physical activity, and electrocardiographic signals.Machine learning algorithms can subsequently transform these data into risk scores, anomaly alerts, and predictive information that may support earlier clinical intervention.The paper proposes a layered IoT-ML framework consisting of sensing, communication, preprocessing, intelligent analytics, risk assessment, and clinical decision-support layers.Particular attention is given to supervised learning, deep learning, anomaly detection, edge intelligence, federated learning, and explainable AI.The review also identifies important challenges involving data quality, interoperability, cyber security, privacy, model bias, computational limitations, clinical validation, and regulatory compliance.The paper concludes that IoT-based machine learning has considerable potential for proactive chronic disease detection, but successful clinical adoption requires reliable sensing, representative datasets, transparent algorithms, privacy-preserving architectures, and rigorous real-world validation.