Proposed ensembling of different machine learning classifiers
Abstract
The risk factors and severity of diabetes can be reduced significantly if a precise early prediction is possible. The robust and accurate prediction of diabetes is highly challenging due to the limited number of labeled data and also the presence of outliers (or missing values) in the diabetes datasets. This paper proposes a robust framework for diabetes prediction where the outlier rejection, filling the missing values, data standardization, feature selection, K-fold cross-validation, and different machine learning (ML) classifiers (k-nearest Neighbour, Decision Trees, Random Forest, AdaBoost, Naive Bayes, and XGBoost) and multilayer perceptron (MLP) were employed. The weighted ensembling of different ML models is also proposed to improve diabetes prediction, where the weights are estimated from the corresponding area under the ROC curve (AUC) of the ML model. AUC is chosen as the performance metric, which is then maximized during hyperparameter tuning using the grid search technique. All the experiments were conducted under the same conditions using the Pima Indian Diabetes Dataset. From all the extensive experiments, our proposed ensembling classifier is the best-performing classifier with the sensitivity, specificity, false omission rate, diagnostic odds ratio, and AUC of 0.789, 0.934, 0.092, 66.234, and 0.950, respectively, which outperforms the state-of-the-art results by 2.00 % in AUC. Our proposed framework for diabetes prediction outperforms the other methods discussed in the article. It can also provide better results on the same dataset, leading to better performance in diabetes prediction. Our source code for diabetes prediction is made publicly available.