Aug 2026· International Journal of Technology and Emerging Research· 0 citations· 33 references
TL;DR
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.
Abstract
Cardiovascular disease (CVD) is still the number one cause of death worldwide and many patients are exposed to severe cardiac events only after the disease has progressed to an advanced stage. Recent advances in artificial intelligence (AI) and machine learning (ML) have shown great potential in improving the early prediction of cardiovascular risk from electronic health records, physiological measurements and other clinical data. This paper provides an analytical review of recent studies on ML-based approaches for early heart disease and cardiogenic shock prediction. The study evaluates the performance of popular algorithms including Logistic Regression, Support Vector Machines, Random Forests, Gradient Boosting Machines, and neural networks. It also investigates the effect of data preparation techniques such as feature scaling, normalisation, and class balancing on prediction outcomes.Results show that ML models are superior to traditional risk score methods in terms of accuracy and can detect high risk patients much earlier than traditional clinical practice. However, data heterogeneity, missing data, model interpretability, and limited clinical validation continue to pose challenges for broad implementation, despite these promising results. The study concludes that 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.
Keywords: artificial intelligence; machine learning; Electronic Health Records; cardiovascular disease; Early Disease Prediction
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
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
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 is currently one of the most significant issues facing the world. One of the most significant illnesses affecting blood vessels and the heart is cardiovascular disease. The toll of death from cardiovascular disease, which is mostly caused by a lack of early disease detection, will be greatly decreased if the risk is predicted beforehand. Anticipating cardiac disease could be a significant medical breakthrough because it is so prevalent. Machine learning approaches anticipate the disease based on the severity of the patient's side effects due to the growing amount of data in the healthcare industry. This study suggests a methodology for early cardiovascular disease prediction using various machine learning techniques for various prediction objectives. Nevertheless, a number of these methods might be enhanced, such as inadequate accuracy. In our research, we have taken the cardicascular diseases dataset and implemented a few models, which include Gradient Boost, Random Forests, and Linear Regression classifiers getting 75.19% accuracy. This work has the advantage of using machine-learning techniques to improve coronary heart disease prediction performance.
Asha Dilipkumar Jariwala, Hemangini Patel· International journal of com...· 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
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
