Advancing Diabetes Prediction: An Ensemble Learning Method for Improved Accuracy using Soft Voting Classifier

Diabetes, a widespread and persistent health concern, arises from inadequate insulin production, leading to elevated blood glucose levels and severe health complications. Factors such as age, lifestyle, physical activity, and dietary habits significantly influence its development. Notably, India stands as a leading hub for diabetes cases worldwide. Utilizing machine learning algorithms, diverse classification techniques contribute to accurate diabetes prediction, offering valuable insights beyond its chronic nature. In this paper, main goal is to estimate and compare different classification models for predicting diabetes. It is observed that the predictive capabilities of Random Forest and K-Nearest Neighbors through soft voting, provided with the highest accuracy. This highlights the effectiveness and superiority of the ensemble approach in enhancing prediction accuracy, making it a compelling choice for diabetes prediction systems. The primary objective of the study is to accurately detect early-stage diabetes by using a soft voting classifier for improving patient care through reliable and more accurate prediction techniques.