Paper Title: Typical and Non-Typical Diabetes Disease Prediction using Random Forest Algorithm
A non-communicable disease Diabetes is increasing day by day at an alarming rate all over the world and it may cause some long-term issues such as affecting the eyes, heart, kidneys, brain, feet and nerves. It is really important to find an effective way of predicting diabetes before it turns into one of the major problems for the human being. If we take proper precautions on the early stage, it is possible to take control of diabetes disease. In this analysis, 340 instances have been collected with 26 features of patients who have already been affected by diabetes with various symptoms categorized by two types namely Typical symptoms and Non-typical symptoms. The purpose of this study is to identify the Diabetes Mellitus type accurately using Random Forest algorithm which is an Ensemble Machine Learning technique and we obtained 98.24% accuracy for seed 2 and 97.94 % for seed 1 and 3.