Abstract
Recently, machine learning models have become a key methodology in detection of cardiovascular diseases (CVD). This gives medical practitioners diagnostic support and indicators. In this work, we compare various machine learning (ML) classification algorithms, apply them to disease dataset and examine how these algorithms perform when subjected to either of the classes to aid in the study and investigation of CVD through computer-aided diagnosis (CAD). Our two main goals in this work are to first offer an automated machine learning ensemble model for categorizing cardiovascular malignancies and second to compare the performance of several classification algorithms to find the best classifier for the task. The proposed technique is specifically developed as a potential support for clinical care based on patient diagnostic data. The proposed approach exhibits an accuracy of 94.28% in the detection of cardiac illnesses when a thorough examination of binary classification is performed and averaged over numerous model training iterations. We believe that incorporating the suggested ensemble methods would produce stable and dependable CAD systems.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Imam, M., Adam, S., Agrawal (Garg), N., Kumar, S., Gosain, A. (2023). An Ensemble Method for Categorizing Cardiovascular Disease. In: Mishra, A., Gupta, D., Chetty, G. (eds) Advances in IoT and Security with Computational Intelligence. ICAISA 2023. Lecture Notes in Networks and Systems, vol 756. Springer, Singapore. https://doi.org/10.1007/978-981-99-5088-1_24
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DOI: https://doi.org/10.1007/978-981-99-5088-1_24
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