An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI

Abstract

Diabetes is a chronic disease that continues to be a primary and worldwide health concern since the health of the entire population has been affected by it. Over the years, many academics have attempted to develop a reliable diabetes prediction model using machine learning (ML) algorithms. However, these research investigations have had a minimal impact on clinical practice as the current studies focus mainly on improving the performance of complicated ML models while ignoring their explainability to clinical situations. Therefore, the physicians find it difficult to understand these models and rarely trust them for clinical use. In this study, a carefully constructed, efficient, and interpretable diabetes detection method using an explainable AI has been proposed. The Pima Indian diabetes dataset was used, containing a total of 768 instances where 268 are diabetic, and 500 cases are non-diabetic with several diabetic attributes. Here, six machine learning algorithms (artificial neural network (ANN), random forest (RF), support vector machine (SVM), logistic regression (LR), AdaBoost, XGBoost) have been used along with an ensemble classifier to diagnose the diabetes disease. For each machine learning model, global and local explanations have been produced using the Shapley additive explanations (SHAP), which are represented in different types of graphs to help physicians in understanding the model predictions. The balanced accuracy of the developed weighted ensemble model was 90% with a F1 score of 89% using a five-fold cross-validation (CV). The median values were used for the imputation of the missing values and the synthetic minority oversampling technique (SMOTETomek) was used to balance the classes of the dataset. The proposed approach can improve the clinical understanding of a diabetes diagnosis and help in taking necessary action at the very early stages of the disease.

Explainable AI Evaluation: A Top-Down Approach for Selecting Optimal Explanations for Black Box Models

“…Remarkably, Glucose, BMI, and Age were recognized as the most salient features [68]. Similarly, another study employed similar methods including RF and XGBoost, and employed LIM and SHAP as explainers [69].…”

Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms

“…For model development, the study cohort was randomly divided to create a 70%:30% training set to test set ratio. Because the number of ESRD cases was much smaller than the number of non-ESRD cases, we performed the synthetic minority over-sampling technique (SMOTE)-Tomek algorithms to balance the number of samples taken for imbalanced data [18,19]. Six machine learning models, including logistic regression, extra trees [20], random forest [21], gradient boosting decision tree (GBDT) [22], extreme gradient boosting models (XGBoost) [23], and light gradient boosting machine (LGBM) [24], are performed.…”

Analyze Important Features of PIMA Indian Database For Diabetes Prediction Using KNN

“…As a result, Choudary focuses on Precision and Recall.. Hafsa Binte Kibria used a ratio of training data : test data is 7:3. In her research, it was found that when using the Random Forest method, the irrelevant feature was glucose, blood pressure, and pregnancy [21]. Vaishali, tried to find important features by using feature selection based on genetic algorithms.…”