Jul 2026· International Conference on Smart Communications and Networking· pp. 1-6· 0 citations· 21 references
Abstract
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.
The creation of an algorithm using machine learning and natural language processing methods is the main goal of this study. Predicting cardiac disease is a tough problem in medical data analysis, given that it is now one of the leading causes of mortality globally. The varity of types such as health records, electronic health records, network monitoring (body), and patients diagnosing data conditions by projecting medical sensors and wearable technology onto the human body are used. Machine learning (ML) has demonstrated its value in aiding decision-making and predicting outcomes from the extensive datasets provided by the healthcare industry. The study presents the innovative MLP-EBMDA (Multi-Layer Perceptron for Enhanced Brownian Motion-based Dragonfly Algorithm) for heart disease prediction and uses an efficient unsupervised method for feature selection. The process begins with obtaining input from the dataset, followed by preprocessing and the proposed feature selection technique, which adeptly chooses relevant features. Subsequently, The MLP-EBMDA is a novel technique is used to classify cardiac disease, making early-stage heart disease prediction easier. The suggested method successfully classifies cardiac disease as normal or abnormal with an excellent impressive accuracy rate of 96.4%.
M. Madhuri, A. S. Babu· 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 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
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
The proposed approach uses a Quantum Neural Network for machine learning in an intelligent Cardiovascular Disease (CVD) prediction system. The diagnosis of heart disease in the early stages is significant, but physicians do not always have enough time to go through the patient's historical data. This system improves medical care by quickly analysing patient records and generating risk predictions with high precision. Data was collected on 815 patients with heart disease symptoms for training and evaluation, and the Framingham study dataset of 5,209 patients was used for validation. Its accuracy rate is 98.5%, and it has the highest sensitivity and specificity in the current literature, matching exact expert opinions. Integrating this decision-support system in medical diagnostics can allow clinicians to personalise their treatment strategies, cutting expenses and enhancing clinical outcomes. This prognostic tool provides up-to-date knowledge and can be used in daily clinical practice to improve decision-making and increase treatment efficiency in cardiovascular medicine. The results validate its advantage over current prognostic systems.
Hutashani B. Rayate, Mangesh D. Nikose, Prakash G. Burade· International journal of 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