Abstract
An unexpected demise due to cardiac arrest is a significant physical anomaly and is responsible for several deaths. Death due to unexpected cardiac arrest has no significant symptoms, and cardiac arrest has no initial symptoms. In most cases, a person may suffer from cardiac arrest, despite having a normal electrocardiogram (ECG). In this work, we have used two concepts, i.e., rough set theory (RST) to find the significant symptoms of cardiac arrest and support vector machines (SVM) to predict cardiac arrest. Our work aims to predict unexpected cardiac arrest less than half an hour before its occurrence. We have validated our claim using statistical techniques.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Hota, R., Dash, S., Mishra, S., Pattnaik, P.K., Pradhan, S. (2024). Early Prediction of Thoracic Diseases Using Rough Set Theory and Machine Learning. In: Udgata, S.K., Sethi, S., Gao, XZ. (eds) Intelligent Systems. ICMIB 2023. Lecture Notes in Networks and Systems, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-99-3932-9_36
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DOI: https://doi.org/10.1007/978-981-99-3932-9_36
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