Advancing hospital healthcare: achieving IoT-based secure health monitoring through multilayer machine learning - Ke Qi (Journal of Big Data)
Abstract Background Data based clinical decision support system is a boon for health care monitoring. Smart healthcare monitoring systems play a vital role in the early diagnosis and detection of the physical and mental health of patients. The smart clinical IoT (C-IoT) systems are data-driven and provide efficient support for this purpose. Purpose There is a need to have a secure, accurate, and efficient HCM system that is capable of processing large amounts of patient data for timely diagnosis and detection of various health complications. Traditional ways of migration are imprecise, less secure, and do not cover all angles necessary in the contemporary healthcare environment. Because of this, the conceptual IoT-based secure health monitoring system employs machine learning algorithms for enhanced accuracy. Method This study presents the conjugate applications of machine learning algorithms with the cloud-based C-IoT model systems. This model is a lightweight encryption block model that maintains provisional security for health and clinical data. It assists in patient’s health issues which are diagnosed with the existing database of the history of that patient and proper measures are taken with proper diagnosis and using this prediction model. The health status is diagnosed from the pre-historical database of the patient’s database. Results This cloud-based smart C-IoT system shows the results approximately with 91% accuracy while using Artificial Neural Network (ANN) algorithms. This smart C-IoT-based health issue diagnostic model is one step ahead toward the modernization of society 5.0. Future prospects The proposed IoT-based secure health monitoring system expands the surgeries of health care by achieving a high diagnostic accuracy of 91% employing ANN algorithms, the excellence of which is founded on data intensity with prior patient data, and the data security by lightweight encryption algorithms. Aligned with Society 5.0, it brings new, friendly, and efficient features to healthcare that replace many existing methods with better ones in terms of precision, security, and coverage.