Jul 2026· International Research Journal of Multidisciplinary Technovation· 0 citations· 26 references
TL;DR
The paper presents the framework that mediates between optimization methods and predictive modeling to provide valuable information on the next generation of data-driven cardiovascular diagnostics to provide valuable information on the next generation of data-driven cardiovascular diagnostics.
Abstract
The proper diagnosis of cardiovascular disease (CVD) risk rises as a significant issue in clinical practice because the risk is directly associated with patient survival. Thus, correct and timely assessment of the risk is crucial. This work suggests a CVD probability prediction model, which combines ensemble learning and Deep Learning (DL) algorithms with a feature choice approach supported by a Modified Genetic Algorithm (MGA). A balanced clinical dataset consisting of 1,025 patient records and 14 medically relevant attributes was used which was retrieved via a publicly available repository with benchmarks dataset. The comparison of a broad range of predictive models, including classical Machine Learning (ML) algorithms like Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors, Naïve Bayes, Decision Trees, Random Forests, XGBoost, and LightGBM, as well as DL models, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Multi-layer Perceptrons (MLP), was made. The MGA based feature collection is used to select the most informative clinical attributes, decrease feature redundancy, and increase discriminative ability. The outcomes of experimental findings prove that the models with the assistance of MGA are most effective as they are more predictive and stable across various classifiers. The results demonstrate the imperative need of intelligent feature selection to enhance model generalization and predictability, which reinforces predictive modelling framework with feature optimization against cardiovascular risk prediction. The paper presents the framework that mediates between optimization methods and predictive modeling to provide valuable information on the next generation of data-driven cardiovascular diagnostics.
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
Background Study: Background Study: Cardiovascular disease (CVD) causes millions of fatalities each year and places a heavy financial strain on healthcare systems. Better patient outcomes, prompt clinical intervention, and lower healthcare costs all depend on early and precise cardiovascular disease prediction. Through the analysis of massive amounts of clinical data, machine learning algorithms have considerable potential in helping doctors identify diseases.
Problem Statement: High-dimensional clinical datasets, repetitive and irrelevant features, and the difficulty to consistently identify the most discriminative risk factors are common problems for current machine learning-based techniques for cardiovascular disease prediction. These problems limit the robustness and generalizability of prediction models, raise computing costs, and decrease classification accuracy. This is particularly true for distance-based classifiers, such as K-Nearest Neighbor (KNN). Developing an efficient approach that combines accurate classification with suitable clinical feature selection remains a critical research problem for improving early cardiovascular disease prediction and enabling reliable clinical decision-making.
Purpose: In a medical decision support system, the prediction of cardiovascular disease is an important task, as early detection can help in minimizing the risk of mortality, delay in treatment, and cost of healthcare.
Methods: In this study, an optimized hybrid approach of the genetic algorithm and K-nearest neighbor method for cardiovascular disease prediction is proposed. The clinical attributes are selected using the genetic algorithm, and the final classifier is KNN. Four datasets, the Cleveland Processed Heart Dataset, the CRPF Ranchi Clinical Heart Dataset, the Cleveland Hungarian Statlog Dataset, and the Heart Failure Clinical Record Dataset, were used for evaluating the model. Initial experiments were conducted with k-fold values of 5, 10, 15, 20, and 25 folds, and then an optimized 10-fold GA-KNN approach with feature selection, normalization, binary target conversion, and hyperparameter tuning of KNN was executed.
Results: The optimized model achieved accuracies of 78.19%, 75.71%, 92.10%, and 81.98%, respectively, with ROC-AUC values of 0.8612, 0.7603, 0.9665, and 0.8313.
Conclusion: It is demonstrated that optimized GA-KNN can achieve both feature dimensions for the initial screening of cardiovascular diseases. The proposed GA-KNN framework is simple, interpretable, and computationally efficient for preliminary cardiovascular disease screening.
Banibrata Paul, Bhaskar Karn· Journal of Intelligent Decis...· 0 citations
Heart disease remains one of the leading causes of mortality worldwide, making early and accurate diagnosis essential for improving patient outcomes. This study proposes a hybrid feature selection and ensemble learning framework for heart disease diagnosis using a publicly available dataset containing 303 records and 13 clinical attributes. The preprocessing pipeline included missing-value imputation, one-hot encoding of categorical variables, and minimum–maximum normalization. Support vector machine–recursive feature elimination (SVM–RFE) was used to identify the most informative clinical features and remove redundant attributes. Multiple machine learning classifiers, including logistic regression, k-nearest neighbors, SVMs, decision trees, random forests, and extreme gradient boosting (XGBoost), were evaluated. Bayesian optimization was applied to tune model hyperparameters, and performance was assessed using an 80:20 train–test split together with stratified five-fold cross-validation. The proposed ensemble model achieved 93.62% accuracy, 94.08% precision, 93.11% recall, 93.58% F1-score, and 0.96 receiver operating characteristic area under the curve. The selected features included chest pain type, ST-slope, exercise-induced angina, maximum heart rate, and age, all of which are clinically meaningful predictors of cardiovascular risk. The results show that combining SVM-RFE, hyperparameter optimization, and ensemble learning improved predictive performance, generalization, and interpretability. The proposed framework can support early heart disease screening and clinical decision-making.
Anurag Tripathi, Rahul Vyas, Gaurav Dwivedi et al.· Artificial Intelligence in H...· 0 citations
Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide, creating a strong need for accurate and scalable prediction systems capable of supporting early clinical diagnosis. Existing studies on cardiovascular prediction often focus on limited datasets or evaluate only a small number of machine learning models, resulting in restricted generalization and inconsistent comparative analysis. To address these limitations, this study presents a systematic comparative evaluation of classical machine learning algorithms, ensemble learning approaches, and hybrid deep learning techniques for cardiovascular disease prediction using a large-scale clinical dataset containing approximately 70,000 patient records. The study evaluates Logistic Regression, Decision Tree, Support Vector Machine, K-Nearest Neighbors, Gaussian Naïve Bayes, Multi-Layer Perceptron, Random Forest, Gradient Boosting, and XGBoost under identical preprocessing and experimental conditions. Data preprocessing includes feature scaling, encoding, data cleaning, feature importance analysis, and class balancing using SMOTE to improve prediction reliability and generalization. Model performance is evaluated using Accuracy, Precision, Recall, F1-Score, confusion matrix analysis, and comparative statistical ranking. Experimental results demonstrate that ensemble learning models consistently outperform traditional approaches, with XGBoost achieving the highest overall performance of 74.80% accuracy and strong recall performance for identifying cardiovascular risk cases. The findings confirm that ensemble learning methods provide improved predictive stability, reduced false negatives, and better clinical applicability for healthcare decision support systems. The study highlights the importance of standardized comparative evaluation frameworks for reliable cardiovascular disease prediction and demonstrates the effectiveness of ensemble-based learning for scalable medical analytics.
Pabbathi Jacob, Vijayakumar, D. Sravan et al.· International Conference Com...· 0 citations
Cardiovascular disease is still one of the leading causes of death, and hence, the early prediction of risk is a very important task in preventive medicine. Although recent studies have shown encouraging results in the application of machine learning algorithms to the prediction of heart disease, it has been noticed that most of the algorithms are more concerned with accuracy-driven optimization than the concerns of safety and false negatives. In medical decision support systems, false negatives are more harmful.This paper presents a light-weight and interpretable machine learning approach for the early risk prediction of heart disease based on structured clinical data. Various models such as Logistic Regression, Random Forest, XGBoost, and stacking ensemble classifiers are compared based on clinically meaningful evaluation metrics such as accuracy, pre- cision, recall, F1-score, and ROC- AUC. The experimental results indicate that ensemble classifiers perform better than individual models, and the unoptimized StackingClassifier performs the best (Recall: 0.8807, F1-score: 0.8930, AUC: 0.9147). Cost-sensitive and threshold-optimized stacking further enhances the recall to 0.9266. To improve the transparency and clinical trust, SHAP and LIME are combined to offer global and local explanations. The findings point out ST depression, maximum heart rate reached, type of chest pain, cholesterol, and exercise-induced angina as the important risk factors. The proposed approach shows that simple and interpretable ensemble models can provide accurate heart disease risk predictions.
S. Shinde· International Journal of Bio...· 0 citations
The results show how a combination of explainable ML and accessible clinical biomarkers can offer a precise, transparent, and clinically interpretable framework for early CKD diagnosis, risk stratification, and informed clinical decision making.
M. Khuntia, Hariballav Mahapatra, N. Lodha· Kidneys· 0 citations