Optimizing Diabetes Classification: BOA-Enhanced ML with EDA and SMOTE

Abstract

Diabetes Mellitus, a chronic metabolic disorder stemming from fluctuations in blood glucose and insulin levels, exerts profound impacts on every organ, significantly compromising overall health. While a permanent cure remains elusive, proactive management can control the disease’s extent. Early detection is pivotal in averting its onset. This research employs Exploratory Data Analysis (EDA), coupled with SMOTE analysis, to unveil patterns, correlation, characteristics, and data structures. For diabetes classification, Support Vector Machine (SVM), Extreme Gradient Boosting (XG Boost). Random Forest (RF), Logistic Regression (LR) and Decision Tree (DT) optimized by Bees Optimization, were employed. Metrics like the F1 Score, ROC curve, accuracy, precision, and recall are used to carefully evaluate the model’s performance. In order to determine the parameters that support classification, this model was tested using the PIMA Indian dataset and real-time datasets. For the real- time dataset with BOA, the SVM model scored an astounding 98.86% accuracy, but for the PIMA dataset, it only managed a 96% accuracy. As a result, this study proves that, in comparison to cutting-edge techniques, combining EDA with SMOTE and ML with BOA produces better outcome.