Jul 2026· International Journal of Science and Research Archive· 0 citations
TL;DR
A machine learning-based framework enhanced with explainability is introduced, built around a structured data preparation process that handles categorical encoding, numerical scaling, and minority class oversampling through the SMOTE technique, positioning it as a trustworthy tool for assisting medical professionals in data-driven clinical decision-making.
Abstract
Diabetes mellitus is a long-term metabolic disorder that typically goes unrecognised until it has already caused significant harm, making timely identification a critical clinical priority. Unfortunately, conventional diagnostic approaches frequently fall short in detecting the disease before it progresses, particularly within busy healthcare settings. To tackle this gap, the current study introduces a machine learning-based framework enhanced with explainability, built around a structured data preparation process that handles categorical encoding, numerical scaling, and minority class oversampling through the SMOTE technique. A pair of classification models, Logistic Regression and Random Forest, are trained, tested, and directly compared to assess their diagnostic reliability. The findings reveal that Random Forest consistently delivers stronger results, reaching a classification accuracy of 98%, which reflects its capacity to learn intricate relationships within real-world clinical data. To move beyond raw performance, SHAP analysis is integrated to shed light on how individual patient attributes shape each prediction outcome. The resulting system strikes a meaningful balance between diagnostic accuracy and model interpretability, positioning it as a trustworthy tool for assisting medical professionals in data-driven clinical decision-making.
It is indicated that a rigorously conducted methodology and interpretability in machine learning development are crucial in creating machine learning solutions in healthcare decision support, which is the pathway to real applications in diabetes risk assessment.
T. Khan, M. Saeed, Majid Hussain et al.· Scientific Reports· 0 citations
Diabetes prediction plays an important role in re-ducing long-term health risks by enabling early medical interven-tion.
Although machine learning models have been widely applied to this task, many existing studies emphasise predictive accuracy
while giving comparatively little attention to the reliability, interpretability, and stability of the resulting decisions. This paper
develops a reliability-aware and interpretable machine learning framework for diabetes prediction from structured clinical data.
Three complementary models—Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost)—are trained
on the Pima Indians Diabetes dataset so that both simple linear and complex non-linear relationships are captured. Beyond
conventional discrimination metrics, the reliability of the predicted probabilities is quantified using the Brier score and reliability
(calibration) diagrams. Interpretability is addressed with SHapley Additive exPlanations (SHAP) at both the global (cohort) and
local (individual patient) levels. Because different models frequently emphasise different predictors, we formalise a Feature
Consistency Index (FCI) that quantifies the cross-model agreement of SHAP-derived feature importance and combines it with
normalised importance into a single ranking score. Finally, a perturbation-based robustness analysis measures the sensitivity of
each model’s output to small changes in the input record. Experi-mentally, XGBoost achieves the highest discrimination
(accuracy 0.7597, ROC-AUC 0.8374), whereas Random Forest attains the best-calibrated probabilities (Brier score 0.1646),
demonstrating that discrimination and reliability are not interchangeable. The FCI identifies Glucose and BMI as
simultaneously the most influential and the most consistently attributed predictors, while Blood Pressure and Skin Thickness are
both weak and unstable. Under a 5% Gaussian perturbation of a representative patient record, the linear and bagged models
shift by less than 0.01 in predicted probability, whereas the boosted model shifts by 0.0386, revealing an accuracy–stability
trade-off that a purely accuracy-driven evaluation would not expose
R. V, S. Sasirekha· International Journal for Re...· 0 citations
Six supervised learning models were developed and compared for diabetes prediction using a dataset and compared for diabetes prediction using a 100k patients records with eight clinical features including gender, age, hypertension, smoking history, heart disease, BMI, HbA1c level, and blood glucose level.
Background: Diabetes mellitus is a major global health burden, and its early detection is essential for preventing serious complications. Machine learning, and eXtreme Gradient Boosting (XGBoost) in particular, performs strongly on routine clinical data; however, reported results are frequently optimistic because of hold-out evaluation and data leakage, and the resulting models are often difficult to interpret.
Objective: To develop and rigorously evaluate an interpretable XGBoost model for predicting the presence of diabetes from eight routine diagnostic measurements, using an unbiased, leakage-controlled evaluation design.
Methods: An openly available dataset of 1,168 patient records (771 non-diabetic and 397 diabetic; an approximately 66:34 class imbalance) with eight clinical features was analysed. Physiologically implausible zero values were treated as missing and imputed with the median inside a processing pipeline. Model selection and performance estimation were separated using a 5×5 nested cross-validation scheme, with all preprocessing confined to the training folds to prevent data leakage. Performance was assessed on out-of-fold predictions using accuracy, sensitivity, specificity, precision, F1 score, the area under the ROC curve (ROC-AUC), and the Brier score; probability calibration was examined; and the contribution of each feature was quantified with SHapley Additive exPlanations (SHAP).
Results: The model achieved an ROC-AUC of 0.823, an accuracy of 0.769, a specificity of 0.859, a sensitivity of 0.595, a precision of 0.684, an F1 score of 0.636, and a Brier score of 0.160. The near-diagonal calibration curve and low Brier score indicated that the predicted probabilities were well calibrated and could be interpreted as reliable risk estimates. SHAP analysis identified glucose, body mass index, and age as the most influential predictors, in agreement with established clinical risk factors for diabetes.
Conclusions: Under a leakage-controlled, unbiased evaluation, XGBoost provided moderate but trustworthy discrimination together with well-calibrated probabilities for diabetes prediction, while SHAP confirmed clinically plausible predictors. The comparatively conservative performance underscores the importance of nested cross-validation over simple hold-out splits for realistic model assessment. External validation and decision-threshold or class-imbalance strategies represent promising directions for future work.
Z. Kucukakcali, I. Cicek· International Journal of Med...· 0 citations
Diabetes mellitus is a chronic metabolic disorder whose global prevalence continues to rise, creating an urgent need for scalable, low-cost tools for early risk identification. This study evaluates the effectiveness of five machine learning algorithms (Random Forest, XGBoost, Support Vector Machine [SVM], CatBoost, and TabNet) for predicting diabetes risk from routinely available clinical and lifestyle variables. Using the Pima Indians Diabetes dataset, a preprocessing pipeline was applied that included median imputation of physiologically implausible zero values, standardized (Z-score) feature scaling, Boruta-based feature selection, and class-imbalance handling through class-weight adjustment and the Synthetic Minority Oversampling Technique (SMOTE). Models were trained on an 80/20 train-test split and assessed using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC-AUC). Random Forest achieved the strongest overall performance (accuracy 0.753; F1-score 0.689; ROC-AUC 0.810), followed by CatBoost, SVM, and XGBoost, whereas TabNet performed worst with very low recall for the diabetic class. The best-performing model (Random Forest) was deployed in a lightweight Flask web application that returns a probability-based diabetes risk assessment, categorising each prediction as low, moderate, or high risk together with a tailored recommendation. The findings confirm that ensemble tree-based methods, particularly Random Forest, provide a reliable, interpretable, and deployable basis for diabetes risk screening, especially in resource-constrained settings. Key limitations include dataset homogeneity, residual class imbalance, and limited feature coverage.
T. Olayinka· FUDMA Journal of Sciences· 0 citations
Diabetes is one of the leading causes of morbidity and mortality worldwide. To avoid difficult management of the condition, there is need to predict early onset of the condition. This study investigated the application of machine learning techniques and exploratory data analysis to forecast early-onset of diabetes using the PIMA Indian dataset. Preprocessing included handling missing values and standardization, leading to the development and evaluation of 5 models which include Logistic Regression, K-Nearest Neighbors, Random Forest, Support Vector Machine, and Neural Network. Exploratory analysis identified glucose concentration, body mass index, and age as the most influential features. Random Forest achieved the highest accuracy (0.74%) while both Random Forest and Logistic Regression attained the best ROC-AUC score of 0.81%. Feature importance analysis emphasized the predictive significance of glucose and BMI, aligning with clinical knowledge of diabetes risk factors. Despite the promising results the study acknowledged limitations related to the PIMA dataset's demographic scope and the moderate complexity of neural networks, highlighting areas for future enhancement. Ethical considerations, including data privacy and algorithmic bias, were addressed to ensure responsible model development.
Abdulrahman Nasiru Sada, Eli. A. Jiya, Yahaya Muhammad Umar et al.· Journal of Science Research...· 0 citations