I. Introduction
Diabetes is a long-term condition marked by hyperglycemia, or high blood sugar, which can result in amputation, cardiovascular disease, and other problems like blindness. There will probably be 642 million diabetic people globally in 2040, predicts the International Diabetes Federation (IDF). Therefore, in order to save priceless human lives, there is a pressing need to identify and forecast the symptoms of diabetes early on [1]. Using machine learning techniques to diagnose this condition is one option. Machine learning has quickly spread over many areas of healthcare. Machine learning algorithms can identify whether a patient has diabetes or not, by uncovering hidden patterns using diabetes data. The purpose of this study is to compare the performance and effectiveness of various machine learning algorithms in predicting diabetes.