Jul 2026· International Conference Computing Methodologies and Communication· pp. 1853-1859· 0 citations· 16 references
Abstract
Heart diseases are among the most common causes of death in the world, which underlines the necessity of early risk evaluation and preventive care. This paper is a proposal of an intelligent Heart Risk Prediction System that applies machine learning methods to predict cardiovascular disease risk with high accuracy. The system combines the structured data preprocessing, feature selection, and training the ensemble model based on the implementation of random forest and XGBoost algorithms to analyses clinical and lifestyle-related features. Recursive Feature Elimination is used to filter the most useful features and enhance the efficiency of the model. The suggested ensemble model is effective in capturing complicated association among risk factors and gives coherent prediction results. Through experimental analysis, the ensemble approach results in a recall of 95.2 a precision of 95.8, and an F1-score of 95.5 which surpasses single machine learning models with an accuracy of 96.4. In addition, Explainable AI (XAI) techniques such as feature importance analysis and SHAP-based interpretation are used to explain the contribution of clinical attributes toward prediction outcomes, improving model transparency and trustworthiness. The scalability and applicability in the real-world situation of healthcare is guaranteed by the modular architecture and secure data management. The findings suggest that the suggested framework can help healthcare workers to recognize patients at risk and allow timely intervention to lessen the consequences of heart diseases.
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 proposed model employs Ensemble Learning techniques, which combine multiple machine learning algorithms to improve prediction accuracy and robustness, and is capable of identifying complex patterns in medical data and classifying patients into stroke-risk categories with high efficiency.
Bhagyashri Patil, Priyadarshini C Patil, Soumya M A et al.· International journal of com...· 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
Healthcare is a major concern, among which heart disease is considered as one of the most important diseases where community has a big concern, when it is matter of accounting the ratio of global mortality rate along with morbidity. As the people are more aware now, and there is also easy availability of data sets, there is possibility of the acceptance of machine learning (ML) methods that make improvements in computational intelligence and have enhanced complete diagnosis of cardiovascular disease prediction and solution. At the current time, the beginning of Explainable Artificial Intelligence (XAI) has solved a problem of limitation that conventional system has of black-box modelling by permitting transparency and interpretability in system that make clinical decision power strong. As healthcare applications demand both predictive accuracy and trustworthiness, the integration of ML and XAI has become an important area of research in intelligent cardiovascular care. This systematic review examines contemporary machine learning methods and explainable AI procedures engaged prediction of heart diseases and classification. By collecting databases from various scientific sources and also ensuing a well-structured examination and while keeping in mind following also all the protocol, deep study is performed using these datasets. Also, all the strategies that must be followed during pre-processing step also prepared along with the approaches that will be applied during feature selection. All the required classification algorithms will also be identified with the calculation for metrics and work on explainability methods will be performed in order to identify the gap in the previous work. Also, emphasis will be also put on use of traditional machine learning methods, deep learning approaches and ensemble learning methods, along with post-hoc explanation that are human-understandable for understanding complex AI models. Some examples of these explainability models are SHAP, Saliency Maps, LIME, Integrated Gradients, attention-based interpretability mechanisms, Grad-CAM etc. This study also reviews and work on strengths, restrictions, gaps and practical consequences of already researched applications that present in real-world for medical diagnosis for better healthcare environments. But this becomes now mandatory to identify main research challenges that researchers are facing due to heterogeneity nature of data, privacy and security issues, imbalance of class, model generalizability, adoption by clinical practitioners, interpretability-performance trade-offs etc. All knowledge can be only gained after studying old research papers and do findings, so this research paper talks about all new developing trends and discuss future probable and research directions for emerging system with more transparency, better reliability, that must focus to benefit patients for the prediction of cardiovascular systems for medical diagnosis. The main aim in this paper is to give platform to the researchers, clinical practitioners and healthcare professionals for knowing the current scenario of cardiovascular disease prediction using machine learning approaches and also by applying explainability AI heart disease prediction models that will ease the process and also make the upcoming system with better progress and more trustworthy, operative and unfailing clinical solutions that makes system decision support one.
Ronak Jain, Sachin H. Patel· International journal of com...· 0 citations
The Heart Stroke Prediction System is a machine learning-based application developed to predict the risk of heart
stroke using patient health information. The system helps users identify potential stroke risks at an early stage through
intelligent data analysis. It considers important health parameters such as age, gender, hypertension, heart disease, BMI,
smoking status, glucose level, and work type. The collected data is preprocessed and analyzed to improve prediction accuracy. A
Logistic Regression algorithm is used to classify whether a person is likely to experience a stroke or not. The model is trained
using historical healthcare data and evaluated with testing datasets. The application is developed using Python and Flask to
provide a simple and user-friendly web interface. Users can enter their health details and receive instant prediction results. The
system also includes a BMI calculator and basic health guidance to improve awareness. It supports early diagnosis and
encourages preventive healthcare measures. The proposed system reduces manual assessment time and provides quick decision
support. It offers an efficient, accurate, and cost-effective solution for heart stroke risk prediction. Overall, this project
demonstrates how machine learning can assist healthcare professionals and individuals in improving early stroke detection and
promoting better health management.
G. Kumar, S. Gafur· International Journal for Re...· 0 citations
Hypertension, commonly known as high blood pressure, is a major risk factor for cardiovascular diseases and premature mortality worldwide. Early detection and prevention are critical in reducing its health impact. This study explores the application of machine learning (ML) techniques to predict the likelihood of hypertension in individuals using clinical and demographic data. A variety of supervised learning algorithms, including Logistic Regression, Random Forest, Support Vector Machines, and Gradient Boosting, were evaluated for their predictive performance [1]. The dataset was preprocessed through feature selection, normalization, and handling of missing values to improve model accuracy.[2] Performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC were used to assess the models [4]. The results demonstrate that ML models can effectively identify individuals at high risk of hypertension, offering a valuable tool for early intervention and personalized healthcare [5]. This approach underscores the potential of artificial intelligence in supporting public health efforts and enhancing clinical decision-making.
Key words: Logistic Regression, Random Forest, Support Vector Machines, and Gradient Boosting.
G. Vamsi, K. Bhargavi· International Scientific Jou...· 0 citations
