Investigation of Type 2 Diabetes Patients using Knowledge Based System

Abstract

This research presents a comprehensive overview of diabetes mellitus prediction utilizing Machine Learning (ML) and Deep Learning (DL) algorithms. Diabetes mellitus is a predominant chronic metabolic disorder and demands early diagnosis and proactive management to mitigate its complications. To address the problem, this research utilized advanced computational techniques specifically ML and DL. The suggested ML and DL algorithms have developed as powerful tools for analyzing diverse medical data sources, such as genomic information and electronic health records to predict the onset and progression of diabetes. Based on applications of many algorithms, including Support Vector Machine (SVM), Random Forests (RF), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) in diabetes prediction. It discusses the extraction of relevant features, data preprocessing methods, and model architectures employed to enhance prediction accuracy. newline