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.
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
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