DPMLT: Diabetes Prediction Using Machine Learning Techniques
Praveen Tumuluru, Lakshmi Ramani Burra, Katuku Krishna Sushanth, Shaik Nagoor Vali, C.H.M.H. SaiBaba, Pachipala Yellamma
Abstract
One of the most frequent chronic diseases is diabetes, which can afflict anyone, regardless of age. When the glucose or sugar level is too high, several diseases attack. Diabetes causes a wide range of issues, adding to a high percentage of diabetic patient re-admission. The purpose of this study is to diagnose diabetes using machine learning techniques. Disease prediction decision-making relies heavily on learning-based models. Learning-based models play an essential role in disease prediction decision-making. Decision Tree, Logistic regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest are the models that were evaluated and compared to each other.