Jul 2026· Network Modeling Analysis in Health Informatics and Bioinformatics· Vol 15· 0 citations· 43 references
TL;DR
A framework that integrates multiple machine learning models and optimization algorithms to enable the early prediction of cardiovascular disease, Parkinson’s disease (PD), and nonalcoholic fatty liver disease (NAFLD) and has high potential for adoption in health-care applications driven by artificial intelligence.
This study presents a Multiple Disease Prediction System (MDPS) that predicts the likelihood of various diseases using patient health information and medical datasets and achieves satisfactory predictive performance across multiple disease categories.
Prachi Kumari· Journal of Intelligent Syste...· 0 citations
Cardiovascular Disease (CVD) remains one of the leading causes of mortality worldwide, emphasizing the need for accurate and early diagnostic solutions. Recent advances in Machine Learning (ML) and Deep Learning (DL) have shown significant potential to support clinical decision-making through data-driven prediction models. This study presents a robust Ensemble Learning (EL) framework for the prediction and classification of CVD by integrating multiple ML algorithms with a DL component. Specifically, an Artificial Neural Network (ANN) is employed as a feature extraction layer prior to ensemble aggregation using techniques such as Random Forest, XGBoost, and LightGBM. The proposed approach is evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. Experimental results on a benchmark dataset demonstrate that the model achieves a high accuracy of 98.8%, outperforming individual classifiers and existing approaches. The integration of ANN-based feature extraction enhances model generalization and reduces prediction error. These findings highlight the effectiveness of the proposed framework for early heart disease detection and clinical decision support.
El Haddad Khadija, A. Bekkari, W. Bouarifi et al.· Engineering, Technology &...· 0 citations
The reviewed studies demonstrated that Deep learning approaches, particularly convolutional neural networks, transformer-based models, and multimodal frameworks, showed improved predictive accuracy when large and diverse datasets were available.
Tehreem Khan, Tayyaba Usman, Ifrah Khalid et al.· Discover Artificial Intellig...· 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
The evolution of intelligent healthcare systems has increased the importance of machine learning techniques in timely prediction of heart disease by using clinical and medical data. In recent years machine Learning techniques have been used widely in disease prediction systems due to their ability to examine complex medical patterns and also for better diagnostic accuracy. Many machine learning classifiers achieve strong predictive performance but still acquire high computational power and longer execution time during their training and testing phases. The ensemble learning algorithms such as Extra Trees, Extreme Gradient boosting and LightGBM have excellent performance in prediction of data due to their high classification accuracy and robustness. The main problem is that these models often suffer from high iteration counts, longer execution time and the inclusion of irrelevant or redundant features which may affect prediction efficiency and the overall performance of a model. To address these limitations, this research paper introduces a Rough Set Theory which is totally based on hybrid maximum value attribute (MVA). Hybrid MVA is a feature selection method that combines cardinality based ranking for categorical attributes and variance based ranking for continuous attributes in order to choose the most relevant features before training the models and predict heart disease in an efficient manner.The proposed models evaluate three techniques that are hybrid maximum value attribute Extra trees (MVA ET), hybrid maximum value Attribute XGBoost (MVA XGBoost) and hybrid maximum value attribute LightGBM (MVA LightGBM). This hybrid MVA method improves the performance of the model and improves computational efficiency by selecting the most relevant categorical and continuous features and removes irrelevant ones. The models are implemented using Python in Jupyter Notebook. The dataset is obtained from Kaggle platform which is titled as “Synthetic Heart Disease Prediction Dataset” that contains 50000 records and 20 features. The standard performance evaluation metrics including accuracy, precision, recall, F1 score and execution time are used to measure the success of the proposed approach. This report compares standard models against hybrid MVA across various train and test data distributions. The experimental evaluation is performed using accuracy, precision, recall, F1-score, execution time and iteration analysis to measure model effectiveness comprehensively and Experimental results show that the hybrid MVA based models outperforms the traditional classifiers in terms of prediction accuracy, computational efficiency and feature optimization. It reduces the feature dimensionality from 20 to 13 features, i.e., a 35% reduction in the input feature space before model training. For the Extra Trees classifier, the average iteration steps decreased from 46,787 to 31,040, corresponding to a 33.7% reduction in computational complexity. Similarly, training and execution time were also reduced in Hybrid MVA Extra Trees. The Hybrid MVA XGBoost model and Hybrid MVA LightGBM reduced the average iteration steps from 4,560 to 2,964, corresponding to a 35% reduction, while also reducing training and execution times. The Hybrid MVA Extra Trees model achieved an average accuracy of 99.07%, while Hybrid MVA XGBoost and Hybrid MVA LightGBM maintained average accuracies of 99.73% and 99.57%, respectively. Among the proposed approaches, Hybrid MVA XGBoost provided the best balance between predictive performance and computational efficiency.
Basharat Ahmad Hassan, Babar Saeed, Khurram Gulzar et al.· International Journal of Inn...· 0 citations
There is an urgent need for explainable, clinically validated and standardised ML frameworks to translate predictive models into routine healthcare practice and improve early detection of cardiovascular disease.
Hanna Rasheed, Arya.K.R Arya.K.R, Ashida.K.A Ashida.K.A· International Journal of Tec...· 0 citations
