Early Diabetes Prediction Using Supervised Machine Learning Techniques

Diabetes is a constant sickness that creates when the pancreas neglects to produce sufficient chemical to manage blood glucose levels, or when the body can't utilize insulin as expected. One of the main characteristics of diabetes, a class of metabolic illnesses, is hyperglycemia. Type 1 diabetes is a hereditary disorder that often appears early in life and has a lengthy incubation period. People with type 2 diabetes have improper insulin sensitivity in their cells. It evolves with time and primarily depends on individual lifestyle choices. Diabetes is the most frequent reason for damage and dysfunction of organs and tissue, which includes heart failure, stroke, kidney failure, and blindness. Early diabetes detection is so essential. This work implements and compares the effectiveness of four machine learning models. The first model implemented is XGBoost Classifier with accuracy obtained as 91%, the second model implemented is Random Forest (RF) with reported accuracy as 90% and the third model is Decision Tree with accuracy 88% and the fourth one is Support Vector Classification (SVC) with reported accuracy as 88%.