Analysis of Synthetic Data Generation Techniques in Diabetes Prediction

The problem of inadequate and class imbalanced data is one of the major problems in the classification tasks. Therefore applying synthetic data generation (SDG) approaches to handle class imbalances can be useful in improving Machine Learning (ML) classifier’s performance. The aim of this work is to explore various SDG approaches to improve diabetes prediction using Pima Indian Diabetes Dataset (PIDD). We have also proposed a hybrid approach of SDG by combining the idea of popularly used SDG techniques Synthetic Minority Oversampling TEchnique (SMOTE) and SVM-SMOTE (Support Vector Machine-Synthetic Minority Oversampling TEchnique), named as SSVMSMOTE. The idea is to divide training data into equal halves and apply SMOTE and SVM-SMOTE separately to sub-training samples. The approach has successfully overcome the limitation of SMOTE and SVM-SMOTE. A set of classifiers namely Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), Gaussian Naive Bayes (GNB), AdaBoost (AB), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), and Light Gradient Boosting (LGM) are trained on the combined resampled training data and tested on hold out testset. The experiment shows that boosting classifiers, XGB, and GB outperformed other considered classifiers. Further, the XGB classifier, with the help of the proposed SDG technique, achieved the highest average accuracy of 0.9415. The proposed approach also achieved promising results in terms of other important evaluation metrics such as F-Scores, AUC, Sensitivity, and specificity. Therefore, such an impressive result of the proposed approach suggests its applicability in the real-life decision-making process.