Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{191983,
        author = {G. VIJAYBASKAR and R. PUGAZENDI},
        title = {Internet of Medical Things (IoMT) Diagnostics and Assistive Machine Learning Algorithms for Cardiovascular Syndromes},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {52-59},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191983},
        abstract = {heart disease is rapidly emerging as a major global public health challenge. Hospitals and medical institutions face significant difficulties in accurately diagnosing and predicting this condition. However, advancements in computing technology now allow the healthcare sector to collect and store continuous medical data, which can be leveraged to support more informed medical decisions. In many modern nations, patient data is collected and stored in digital form. This data is then analyzed to facilitate critical medical assessments such as diagnosis, treatment planning, image analysis, and prediction. The use of machine learning algorithms has become increasingly important in tackling complex and unpredictable classification and prediction tasks. Moreover, combining various machine learning techniques helps to improve accuracy of both prediction and classification. This research achieves an expected accuracy in predicting cardiac diseases by combining multiple Machine Learning techniques. The results indicate that this ensemble approach surpasses the accuracy of individual classifiers when compared to earlier studies.},
        keywords = {heart disease, machine learning, Internet of Medical Things},
        month = {February},
        }