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Mr. Palanivel R is an Assistant Professor in the Department of Artificial Intelligence and Data Science at Nitte Meenakshi Institute of Technology (NMIT), Nitte (Deemed to be University), Bengaluru, Karnataka, India. His research interests include Artificial Intelligence, Data Science, Machine Learning, healthcare analytics, predictive modeling, and intelligent computing. He is actively engaged in teaching and research related to AI-enabled solutions for real-world applications.
The amalgamation of Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Big Data Analytics, and wearable health technology has revolutionised contemporary healthcare through enhanced disease diagnosis, ongoing patient surveillance, and proactive disease forecasting. Traditional healthcare frameworks predominantly depend on intermittent clinical assessments and laboratory analyses, frequently resulting in postponed disease identification and intervention. AI-powered predictive analytics combined with IoT-enabled medical apparatus facilitates the ongoing gathering and examination of physiological metrics including heart rate, blood pressure, body temperature, oxygen saturation, blood glucose concentrations, electrocardiograms (ECG), electroencephalograms (EEG), and physical activity. Machine Learning (ML), Deep Learning (DL), Internet of Medical Things (IoMT), Edge Computing, Digital Twins, and Explainable Artificial Intelligence (XAI) enable precise disease forecasting, risk evaluation, tailored therapy, and clinical decision assistance. This chapter delineates the principles, framework, artificial intelligence methodologies, applications, obstacles, and prospective research avenues for AI and IoT in disease diagnosis and early prediction.