Enhancement of Diabetes Prediction Accuracy using Various Machine Learning Algorithms

Diabetes Mellitus is an illness caused by a ineffective insulin management in the human body. Untreated diabetes, characterized by elevated glucose levels, poses serious health risks, including heart complications, kidney dysfunction, hypertension, vision impairment and multi-organ damage. Early prediction is vital for effective intervention. This project aims to enhance diabetes prediction accuracy by employing diverse machine learning techniques such as Logistic Regression(LR), SVM, GBDT and Random Forest (RF) on patient datasets. Risk factors such as age, obesity, sedentary lifestyle, genetic predisposition, poor dietary habits, and hypertension contribute to diabetes mellitus. Recent techniques, like ensemble learning and advanced hyper-parameter tuning, have been explored to improve predictive accuracy. However, challenges such as handling imbalanced datasets, feature selection, and model over-fitting persist. Hospitals employ comprehensive data analysis for precise diagnosis and tailored treatment plans. The primary objective of this project is to build a reliable, accurate model by overcoming these challenges through optimized algorithm selection, feature engineering, and rigorous cross-validation.