Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
The proposed approach uses a Quantum Neural Network for machine learning for machine learning in an intelligent Cardiovascular Disease (CVD) prediction system that has the highest sensitivity and specificity in the current literature, matching exact expert opinions.
Abstract
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.
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 term heart disease refers to the range of conditions that impacts heart and its blood vessels in a negative manner. This has led to increase mortality rate worldwide and a major health issue in recent years and continues to have increasing statistics in future. Modern day lifestyles and choices have made it increasingly common, not only in aged population but also in younger adults. New generation advanced medicines are fortunately a breakthrough in assisting and curing the disease, but conventional techniques are only optimal to provide on the spot single data reading and result, it fails to predict the future circumstances and possible emergency which may be caused due to underlying issue, or provide with early diagnosis to facilitate prompt medical assistance. This is where Machine learning (ML) comes into the picture, delivering the required and necessary early analysis of cardiovascular disease (CVD) through validating various features among thousands of data in a dataset. This study has developed predictive models and used Cleveland dataset with 11 different features namely age, sex, cholesterol, resting heart rate, exercise-induced angina and other health-related indicators that make up this dataset. There are four different predictive models that we will be using here to predict the health analysis of heart related disease, which are Logistic Regression, Random Forest, Support Vector Machines (SVM), K-Nearest Neighbor (KNN). All of the mentioned models are developed and used under Google colab platform. There are 4 different datasets consisting of Cleveland, Hungary, Switzerland and VA Long Beach which are preprocessed and cleaned and then being used and tested here with machine learning models to test its accuracy and other result aspects. The best result and performance among these four models tested across each of the datasets and having minimum spread (max-min) is of Support Vector Machine (SVM). with an F1 score of 0.7732. This study will focus on developing multi predictive models to help pave a way to ensure early diagnosis and provide early treatment.
Keywords— heart; disease; predictive; machine learning; models
Unknown authors· International Journal of Cre...· 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
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
Experimental results demonstrate that Machine Learning techniques can effectively predict disease occurrence with high accuracy, thereby assisting healthcare professionals in early diagnosis and treatment planning.
Sunidhi, Rahul, Parmod Kumar et al.· International Journal for Re...· 0 citations
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