Jul 2026· Recent Advances in Computer Science and Communications· 0 citations
Abstract
Diabetes is a serious condition that requires early diagnosis to minimize the long-term effects, but the traditional methods of diagnosis usually diagnose it at a late
stage. Machine learning has potential, but generic models often are not able to accommodate
population-specific health variations.
We present a machine learning model in the form of an ensemble that uses (i) normalization to standardize heterogeneous medical data, (ii) location-specific corrections and correlation-based feature reweighting to focus on clinically relevant predictors and (iii) a combination of Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting
(XGBoost) using a soft-voting system. It was tested on the PIMA Indians Diabetes Dataset,
where the performance was measured in terms of confusion matrices, ROC curves, and standard measures
The ensemble structure proved to be better at predicting than baseline models (Logistic
Regression, Naive Bayes, and KNN). XGBoost was most accurate and had the highest AUC
(89.1 and 0.93) with individual classifiers, whereas the ensemble misclassified fewer individuals and had better recall among minority (diabetic) cases.
Through preprocessing with location sensitivity and feature weighting with correlation, the proposed framework is successful in capturing subtle risk patterns and implicitly
solving the problem of class imbalance. It has high stability and can be generalized, making it
appropriate to implement in the real healthcare setting
The ensemble-based model proposed provides a viable and scalable model to predict diabetes at an early age. The next step in the work will be the extension of the model to real-time patient data and its implementation in clinical and mobile health platforms to become
more widely accessible.
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
The study comes to the conclusion that headline accuracy is an unreliable guide in imbalanced medical prediction, that imbalance handling can change a model's practical usefulness, and that this benefit is strongly algorithm-dependent, meaning that the decision to resample should be based on the algorithm and the screening priorities rather than being applied consistently.
A. Oduroye, Temilade Opanuga, Esther Tosin Akanbi et al.· International journal of re...· 0 citations
Liver disease is a serious health problem prevalent in the world. Early diagnosis is very important for enhancing quality of patient's health and reducing mortality. Traditional diagnostic techniques tend to take quite long time, be expensive and rely heavily on the skill of the medical personnel. That's why intelligence systems are needed that can make decisions based on data. This study suggests effective framework based on machine learning which allow to make accurate prediction about the liver disease. The methodology includes data preprocessing, which involves such steps as encoding categorical data, filtering out irrelevant information, as well as dealing with missing data. Then Random Forest and Recursive Feature Elimination can be used for feature selection. We proposed a framework to evaluate the performance of various machine learning classifiers such as Logistic Regression, K-Nearest Neighbor (KNN), Decision Tree, Linear Discriminant Analysis (LDA), Multilayer Perceptron (MLP) and many ensemble learning models including Random Forest, Extra Trees, Gradient Boosting, AdaBoost, and Extreme Gradient Boosting (XGBoost). To increase the prediction accuracy, we have developed a two-level ensemble stacking model with multiple ensemble classifiers as base learners and an advanced meta-learner to make predictions. The models have been assessed with accuracy, precision, recall, specificity, F1 score, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), confusion matrix and Cross validation. The experiments conducted showed that the proposed two-level ensemble stacking framework is better compared to both individual classifiers as well as conventional ensemble techniques since it attained an accuracy close to 94%.
Nikitha Jc, H. R, P. G et al.· International Conference Com...· 0 citations
Diabetes affects over 101 million people in India, with many more at risk due to routine and hereditary factors. Early diagnosis is crucial to prevent complications, which make accurate predictive tools essential in healthcare. This research uses Machine Learning (ML) algorithms to evaluate the likelihood of Type 2 Diabetes Mellitus (T2DM) using lifestyle and family history data. The trained models demonstrate strong predictive ability, allowing individuals to self-assess their risk and supporting healthcare professionals in early detection and intervention. This study presents a performance assessment of seven ML classifiers: Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), k-Nearest Neighbor (k-NN), and Extreme Gradient Boosting (XGBoost). These classifiers were applied to the widely used PIMA Indian Diabetes dataset (PIDD), which contains 768 clinical records of adult women aged 21 and above, providing key medical information for diabetes analysis. Multiple evaluation measures were applied to assess model performance with results showing that SVM achieved the highest accuracy and AUC, while LR, RF, and XGBoost also performed competitively. Although k-NN attained the highest recall, it yielded a higher false positive rate. These findings highlight that no single model is perfect for every situation, and the choice of classifier should match clinical needs. This study serves as a reference for ML applications in diabetes prediction.
Rizwan Akhtar, Muhammad Kalamuddin Ahamad· ITEGAM- Journal of Engineeri...· 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
The results demonstrate that ensemble learning structures, which combine the strengths of multiple models, can provide more reliable decision support in critical areas such as healthcare and emphasise that ensemble learning structures, which combine the strengths of multiple models, can provide more reliable decision support in critical areas such as healthcare.
Yasin Karakuş, Pınar Özen· Journal of Innovative Engine...· 0 citations