Jul 2026· International Conference Computing Methodologies and Communication· pp. 1034-1041· 0 citations· 21 references
Abstract
The increase in the prevalence of diabetes and cardiovascular diseases in the world requires accurate and easy to access diagnostics. These chronic diseases are asymptomatic and need early screening in an attempt to prevent complications and reduce healthcare expenditure. The contemporary systems yield tremendous data about patients, their demographics, lifestyle, medical history, lab results, and ECGs, and the traditional tools are processing these data separately. Such a fragmented approach lacks the ability to capture complex risk interactions and predictively forecast the likelihood, slowing down intervention, and putting strain on resources in high-volume or constrained environments. Our system solves this by providing a full preprocessing pipeline: data cleaning, feature scaling, temporal slicing, and missing value imputation of consistent and reliable inputs. It uses a hybrid modelling model based on XGBoost and TabNet with a rule-based logic module with clinical heuristics. The Multimodal Transformer Fusion layer is central, as it dynamically weighted and combined predictions across all modalities so that cross-model interactions could be used to provide context-related outputs. This provides subtle patient particular predictions, enhancing accuracy of diagnosis in profiles. One of the best is the mechanism of clinician-in-the-loop feedback. It provides SAP and SHAP explainability visuals, post-prediction, which clearly explain the contribution of features. Clinicians perfect the model based on practical knowledge, which includes AI and human intelligence that increases reliability and minimizes human supervision. Focused on clinicians, diagnostic laboratories, health professionals, and health-tech innovators, this explainable and easy-to-use platform allows detecting the disease at an early stage and designing a unique treatment regimen.
The problem of early detection of cardiovascular risk is still a powerful challenge because the interplay of physiological, behavioral, and clinical factors involved in cardiac conditions is rather complex. In many health care settings, risk analysis is usually put aside until the symptoms have worsened, which limits the possibility of taking timely action. The current paper presents a proposed intelligent clinical decision-support system that employs monitored machine-learning methods to measure cardiovascular risk based on traditional clinical measures. The proposed system does not rely on the disconnected parameters of the medical condition of a patient but rather asks multidimensional patient data to reveal the non-linear relationship that is often missed by conventional diagnostic tools. The framework also involves systematic data cleaning, feature- relevance analysis, and model optimisation to enhance predictive consistency. Several classification algorithms such as tree based classification algorithms and probabilistic classification algorithms are considered to provide reliability and generalization. The deployment architecture is lightweight, which supports real-time risk scoring, making the system suitable in practice clinical environment. Reconfiguring ordinary clinical information into actionable risk insights, the suggested approach also lies at the core of early-stage intervention, enhancing the effectiveness of the diagnostic process and progressing the creation of data-driven intelligent health-care systems.
M. Menaka, R. C. Kumar, Nathella Adithya Ram et al.· International Conference on...· 0 citations
Diabetes is a major risk factor for the development of cardiovascular issues which contribute to cardiovascular disease (CVD) being a leading cause of mortality worldwide. However, traditional machine learning methods are not widely adopted in healthcare systems because they lack interpretability, which is important for early and accurate CVD risk prediction and for ruling out effective clinical intervention. In this research, a hybrid architecture is proposed that incorporates diabetes related datasets as well as explainable artificial intelligence (XAI) methodologies that could improve the prediction power and transparency of the models. The proposed approach combines different datasets at the level of features and includes rigorous data pre-processing to detect metabolic and cardiovascular risk factors. Some of the significant clinical parameters are age, BMI, glucose, cholesterol, and blood pressure. These are standardized to create a single dataset which may be utilized for predictive modelling. The employment of two XAI approaches, SHAP (SHapley Additive Explanations) with tree-based ensemble models and integrated gradients with transformer based topologies, ensures both performance and interpretability. The technique improves confidence and usefulness in clinical settings by offering accurate predictions and explanations for the model’s judgments that are relevant to the circumstance. It is also utilized for visual investigation of clinical correlations of diabetes and cardiovascular disease and identify crucial risk variables. The results suggest that merging explainability approaches with powerful machine learning can considerably boost early identification and risk assessment. The proposed approach contributes to enhanced healthcare decision-making, offering a scalable, interpretable and dependable solution for cardiovascular disease prediction.
K. Deepthi, P. Bhargavi· International journal of com...· 0 citations
Cardiovascular disease (CVD) remains one of the major causes of morbidity and mortality worldwide, creating a strong need for accurate, scalable, and interpretable computational methods for early detection and risk prediction. This study proposes an Optimized Ensemble Machine Learning Framework (OEMLF) for CVD detection, classification, and early prediction using integrated cardiovascular big data. The framework combines heterogeneous records from the Framingham Heart Study, UCI Heart Disease, Kaggle Heart Disease, and NHANES datasets into a unified repository. The proposed dataflow incorporates data harmonization, duplicate removal, missing-value imputation, outlier handling, normalization, and SMOTE-based class balancing. Clinically meaningful features are subsequently generated, while Mutual Information, SHAP-based feature importance, and Recursive Feature Elimination are jointly employed for hybrid feature selection. Bayesian hyperparameter optimization is then used to tune Logistic Regression, Random Forest, XGBoost, and LightGBM models. Their complementary predictions are integrated using a weighted soft-voting ensemble to improve robustness and generalization across heterogeneous patient records. Experimental evaluation demonstrates that the proposed OEMLF achieves 98.84% accuracy, 98.8% precision, 98.7% recall, 98.7% F1-score, and 99.8% ROC-AUC, outperforming the individual baseline models. The framework also reduces the feature representation from 45 standardized attributes to 40 informative features while eliminating missing values and improving class balance from 1:3.4 to approximately 1:1.1. Computational analysis indicates that although ensemble training requires additional processing time, the framework maintains a low patient-level prediction time of approximately 4.8 ms. SHAP-based explainability further identifies influential cardiovascular factors and provides patient-specific explanations for model decisions. The results indicate that the proposed framework can provide an effective combination of predictive accuracy, scalability, computational efficiency, and interpretability for large-scale cardiovascular screening and clinical decision-support applications.
Amar Singh, Yogesh Mohan· International journal of com...· 0 citations
An epidemic survey of recent years has reached a conclusion that virtually 17.9 million lives are lost annually due to disorders of the cardiovascular system and that these are the biggest killers worldwide. The power to predict cardiac defects early may have potential to decrease morbidity and provide the opportunity for timely therapeutic interventions. This study proposes an integrated computational model that incorporates traditional pattern recognition methods with neural networks using a hierarchical approach to model aggregation, with transparent mechanisms of decision attribution. Specifically, the proposed system combines a multi-layered feed-forward neural network (NN) with batch normalization and dropout layers with nine different classification algorithms--Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Tree, Support Vector Machine, KNN, XGBoost, LightGBM, and CatBoost--and benchmarked them on various datasets. The best-performing models were then integrated using a hierarchical stacking model: a Logistic Regression supermodel decides whether the test sets shall be combined or not, and which members of the stacking set should indeed be in the final model. Evaluated on the Cleveland cardiac dataset on UCI repository, the proposed model can achieve 90.16% classification accuracy, 96.43% sensitivity, and receiver operating characteristic of area under the curve 0.9524 with 303 clinical records in 13 physiology attributes. Oversampling with SMOTE helps to overcome the natural class imbalance found in the training partition, and Shapley-value-based attribution provides for each patient a fine-grained reason for each prognosis prediction. The Streamlit front-end application provides a fully functional web application to help with a fast bedside risk assessment. Empirical results show that the composite model architecture consistently outperforms individual classifiers by using the minimal possible rate of overlooked positive diagnosis (one), which proves to be very critical in clinical screening situations where negative diagnosis if overlooked has life-threatening consequences.
Sowmya Koppadi, Krishna P S V, V. P· International Journal of Com...· 0 citations
Diabetes is a serious chronic disease affecting millions of people in the world, and if not managed properly, can cause a lot of serious health problems. The challenges of traditional healthcare systems are unable to properly manage real-time patient monitoring, temporal healthcare analysis and accurate disease prediction. This study aims to solve the issues with a hybrid approach that combines Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks and ensemble learning techniques for effective management of chronic diabetes. The design includes the use of healthcare data from wearable sensors, Internet of Things (IoT) devices, and Electronic Health Records (EHRs) that will be used to measure glucose level fluctuations and forecast future health status. The healthcare data is preprocessed for better predictive accuracy and data quality through methods such as normalization, outlier elimination, and feature extraction. The ensemble mechanism fuses the advantages of both the RNN model and the LSTM model to boost the accuracy and robustness of the prediction results. The proposed method achieves an accuracy of 98%, precision of 92%, recall of 87%, F1-score of 92% and Mean Squared Error (MSE) value of 1 with better results than the traditional databases, rule-based system, statistical approaches and standalone machine learning models. The proposed framework is designed to facilitate real-time monitoring, personalized diabetes management recommendations and enhanced clinical decision making for chronic diabetes management.
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.
N. J, Deekshitha U, K. V· International Journal of Sci...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
