Machine Learning-Driven Internet of Things (MLIoT)-Based Healthcare Monitoring System
Kutubuddin Sayyad Liyakat Kazi, Mahesh Ashok Mahant · Advances in medical technologies and clinical practice book series · 2024
Healthcare organisations frequently use Machine Learning(ML) to generate precise and timely results. Doctors can make preliminary choices to save patients' lives thanks to early disease predictions. The power of Machine Learning(ML) applications in healthcare is being increased thanks in part to IoT- Internet of Things. ML-Machine learning techniques are utilised to analyse the data collected from IoT sensors about patients. The principal objective of the endeavour is to develop ML-based healthcare Framework that can reliably and early identify various diseases. Adaptive boosting, Random Forest, Decision Trees, Support Vector Machines, Naïve Bayes, Artificial Neural Networks & K-Nearest Neighbor are seven ML classification algorithms used in this work to predict nine fatal diseases: Blood Pressure, Diabetes, Hepatitis, and Kidney Disorders. The performance metrics—likes Accuracy, Precision, and Recall stand used to assess effectiveness of suggested model.