Futuristic Diabetes Prediction: Employing Machine Learning for Timely Diagnosis

Diabetes is a prevalent circumstance that is also the core cause of numerous serious health issues, such as neurological disorders, heart attack, and stroke. So that it is placing a growing burden in the global healthcare system. The challenge lies in the accuracy of diabetes prediction model. This study centers on constructing an autonomous diabetes prediction system using a private dataset, tackling class imbalance through SMOTE techniques. Leveraging machine learning algorithms the research identifies the most effective prediction method. Feature selection algorithms guide the choice of relevant features, and the system's performance is rigorously evaluated. Remarkably, the developed model achieves impressive metrics with a success rate of 91.41%, showcasing the potential of machine learning in enhancing the prediction accuracy. These findings add vital insights to the profession, emphasizing the need of early intervention in diabetes control.