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@article{202177,
        author = {Abhishek Singh and Dr. Anil Kumar Mishra},
        title = {Systematic Review of Artificial Intelligence and Machine Learning For CVD’s},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {6770-6780},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202177},
        abstract = {cardiovascular diseases (CVDs) are one of the major causes of death worldwide and so CVD,s are major challenges for healthcare systems today. Early prediction and prompt diagnosis of heart disease is essential both to bringing down the mortality rate and to improve patient outcome. In the recent past, it has been found that artificial intelligence (AI) and machine learning (ML) techniques have become powerful tools for analyzing medical datasets and assisting in clinical decisions. This study depicts a systematic review of latest advancements in machine learning and artificial intelligence methods in the prediction of cardiovascular diseases (CVD,s). A thorough literature analysis of the twenty latest research studies released between 2022 to 2026 is carried out based on different predictive models such as logistic regression, support vector machines, decision trees, random forests, ensemble learning, or deep learning architectures. Traditional machine learning classifiers like random forest and gradient boosting have proven to be effective in delivering good predictive accuracy in structured clinical data [1] -[3]. Furthermore, deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) exhibited improved ability to extract complicated patterns from physiological signals and electronic health record [4]-[6]. Recent research is also featuring the enthusiasm of the use of explainable artificial intelligence, wearable health monitoring devices, and Internet of Medical Things (IoMT) technologies for the real-time cardiovascular risk assessment. The reviewed studies highlight important research challenges such as data imbalance, model interpretability, dataset limitations and integrating predictive models into clinical settings. This review covers a detailed summary of recent research trends, commonly utilized datasets, machine learning techniques, and future research opportunities of artificial intelligence (AI) driven cardiovascular diseases prediction systems.},
        keywords = {Artificial Intelligence, Machine Learning, Cardiovascular Disease prediction, heart disease detection, Deep Learning, Healthcare Analytics, Explainable AI, Internet of Medical Things (IoMT).},
        month = {May},
        }