Jun 2026· 2026 Third International Conference on Innovations in Cybersecurity and Data Science (ICICDS)· pp. 918-926· 0 citations· 28 references
Abstract
Most current clinical decision support with health care and machine learning are developed for a single disease and are therefore not integrated into the workflows when the patient needs to be assessed for multiple diseases. We present a single prediction architecture aimed at solving diabetes, heart disease and Parkinson's in a single integrated pipeline to overcome this limitation. However, less than 1,300 original records are provided by the three publicly available UCI benchmark sources, which is not enough to allow stable multi-model training, so we created a 50,000 -sample research dataset using merely statistical augmentation with a distribution-aware approach inside the training fold, without using real patients; this is not a 50,000 sample set of distinct real patients, it was only created for the purpose of controlled benchmarking. All the performance statistics mentioned in this document are taken from the original benchmark records that were held out, and not modified during the augmentation process. Four of these classifiers (Support Vector Machine (SVM), Decision Tree (DT), Artificial Neural Network (ANN) and Random Forest (RF)) were trained and evaluated using a common and strictly controlled experimental setup. For the three tasks, RF outperformed all the other algorithms with accuracies of $\mathbf{9 1. 9 \%,\ 8 9. 3 \%}$, and $\mathbf{9 6. 2 \%}$, and with a mean AUC-ROC of 0.955. Repeated stratified splits and paired significance tests ($p<0.05$) show that these numbers are constant between runs. One path that can be taken toward deployment is demonstrated with a Flask based web prototype. The numbers listed here are representative of a research prototype only and should not be used as evidence of clinical readiness.
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.
Diabetes mellitus is a common type of metabolic illness that is very common worldwide, and in most cases, it results in serious effects like heart disease, kidney disease, and blindness. Proper and early diagnosis of diabetes is essential to intervene on time and have better patient outcomes. Machine learning (ML) paradigms provide effective predictive modeling solutions to healthcare, but most of the current literature is limited due to imbalanced datasets, using a single training test split, and limited model interpretability, which diminish their clinical usability. This research paper has introduced a powerful and explainable ML model to predict diabetes based on the Pima Indians Diabetes Dataset acquired via Kaggle, which contains 768 patients with eight clinical variables and a binary response. To counter the class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is used to create natural synthetic samples of the minority diabetic group that facilitate balanced learning without degrading the correlations between the features. Four classifiers, including Logistic Regression, Naive Bayes, AdaBoost, and XG Boost, are trained and tested. The stratified 10-fold cross-validation is used to provide a stable and generalizable model performance, as opposed to using only one data split. The measurement criteria are accuracy, precision, recall, and F1-score, especially for the minority diabetic class. The interpretation of the model is improved by the use of logistic regression coefficients and SHAP (SHapley Additive exPlanations) values, as they allow transparent identification of clinical features that are critical to making predictions. The results of the experiment show that the suggested framework attains an overall accuracy of approximately 94% on an unseen test set, with strong precision and recall of the minority class, thus proving that the combination of class balancing, cross-validation, and explainable ML results in the outcomes of reliable and clinically credible predictions. All performance results are evaluated on an untouched original test set, while SMOTE is applied strictly within cross-validation folds to prevent data leakage. Unlike many existing studies, the proposed framework ensures leakage-free validation, robust cross-validation, and integrated interpretability for clinically meaningful prediction. Although synthetic sampling improves minority class learning, the model is evaluated carefully to ensure generalization on real-world data. This paper indicates 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
This paper proposes a machine learning–based multi-disease prediction system that integrates disease-specific classifiers within a unified, real-time clinical decision-support platform. The framework employs Support Vector Machine (RBF) for diabetes prediction, Support Vector Machine (linear) for heart disease, Decision Tree for chronic kidney disease (CKD), and Logistic Regression for cancer prediction, with each classifier selected according to the statistical characteristics of its respective dataset. The system is implemented using a Streamlit-based web interface, enabling efficient real-time prediction with interpretable outputs. Experimental evaluation demonstrates strong predictive performance, achieving accuracies ranging from 85.71% to 94.30% and AUC-ROC values between 0.91 and 0.97 across the four disease modules, representing a 7.2 percentage-point improvement over comparable unified prediction systems reported in the literature. The modular architecture provides scalability, low computational complexity, and rapid inference, making it suitable for pre-diagnostic screening in clinical environments. The proposed framework offers an effective and practical solution for early chronic disease detection while supporting future expansion. Planned enhancements include the integration of deep learning models for medical imaging and electronic health records, Explainable Artificial Intelligence (XAI) techniques such as SHAP and LIME, wearable and IoT-based continuous health monitoring, federated learning for privacy-preserving distributed model training, and prospective clinical validation through hospital information system integration. These developments are expected to improve prediction accuracy, interpretability, scalability, and clinical applicability for next-generation intelligent healthcare systems.
Chandrasekar.M, Y. S, Adithiyaa K.B· 2026 International Conferenc...· 0 citations
Coronary Heart Disease (CHD) has remained one of the foremost causes of death in the world, and thus, there is a need to ensure that there are dependable early diagnosis mechanisms that would aid clinicians in making decisions at the right time. The rapid development of electronic health records and sensor-based medical data has presented more opportunities in predictive analytics in healthcare than ever before. However, the sensitivity, complexity, and scale of health data require robust analytical models and a safe and reliable data processing system. In this respect, machine learning (ML) methods have become effective instruments in deriving significant patterns of heterogeneous healthcare data. This study hypothesizes an ensemble learning framework that is used in the early identification of CHD. The proposed ensemble model is more accurate and stronger in predictions than any of the individual models by incorporating several ML classifiers. The study provides a scalable method to prevent cardiovascular diseases, and the model may help healthcare professionals to identify high-risk patients at an early stage and, thus, implement interventions in time and enhance patient outcomes. Experimental results demonstrate that the ensemble model outperforms conventional ML models, highlighting its effectiveness as a supportive diagnostic tool for CHD prediction.
Sania Batool, Muhammad Hassan Jamal, Warisha Siddiqui et al.· International Conference on...· 0 citations
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.
C. J, R. Mohanty, Sarappadi Narasimha Prasad· Recent Advances in Computer...· 0 citations
The proposed approach uses a Quantum Neural Network for machine learning in an intelligent Cardiovascular Disease (CVD) prediction system. The diagnosis of heart disease in the early stages is significant, but physicians do not always have enough time to go through the patient's historical data. This system improves medical care by quickly analysing patient records and generating risk predictions with high precision. Data was collected on 815 patients with heart disease symptoms for training and evaluation, and the Framingham study dataset of 5,209 patients was used for validation. Its accuracy rate is 98.5%, and it has the highest sensitivity and specificity in the current literature, matching exact expert opinions. Integrating this decision-support system in medical diagnostics can allow clinicians to personalise their treatment strategies, cutting expenses and enhancing clinical outcomes. This prognostic tool provides up-to-date knowledge and can be used in daily clinical practice to improve decision-making and increase treatment efficiency in cardiovascular medicine. The results validate its advantage over current prognostic systems.
Hutashani B. Rayate, Mangesh D. Nikose, Prakash G. Burade· International journal of com...· 0 citations