Investigation of Healthcare Tracking System for Diabetic Patients Utilizing Machine Learning Algorithms to Integrate the Internet of Things

People in modern world suffer from a multitude of diseases, the most common of which is diabetes, which affects most senior citizens. Diabetes is associated with kidney problems, cancer, and heart disease. Combining IoT with artificial intelligence to improve the functionality of remote systems for monitoring health for doctors, patients and patient attendants is one of the most significant technological advancements to date. The subsequent measures were utilized to assess the efficiency of the different machine learning algorithms in this proposed system: accuracy, sensitivity, specificity, recall, and precision. The different machine learning algorithms are compared with one another: The algorithms used include Logistic Regression (LR), ASV-RF, Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Deep Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Sequential Minimal Optimization (SMO). In comparison to all other algorithms, the ASV-RF algorithm attained a high degree of accuracy of 99.86%.