The Absolute Predictor: An SVM-Driven Paradigm for 100% Accurate Diabetes Insight

This study presents the development of a Support Vector Machine (SVM) model for predicting diabetes using various clinical features. The dataset underwent preprocessing and feature selection to enhance model performance. The evaluation of the model's accuracy was conducted through a confusion matrix, which revealed a perfect classification rate of 100%. Additionally, the analysis included Partial Dependence Plots illustrating the relationship between Body Mass Index (BMI) and predicted diabetes, as well as square plots depicting True Positive Rates (TPR), False Negative Rates (FNR), Positive Predictive Values (PPV), and False Discovery Rates (FDR). The results highlighted glucose levels,BMI, and age as the most significant predictors of diabetes. This research underscores the potential of machine learning models like SVM in improving early detection and intervention strategies for diabetes management, providing valuable insights into clinical decision-making processes.