Abstract
Cosmopolitan lifestyle and livelihood modifications have marked a toll on human health to the extent of myocardial disease onset at a relatively tender stage. One of the major issues that have been observed on the rise is the arterial blockage leading to myocardial infarction. Immune response to the arterial damage or the pericardium is termed as Dressler syndrome. This study focuses on prediction of Dressler syndrome based on myocardial infarction historical data. Moreover, the study focuses on prediction using a resource constraint dataset through six popular machine learning (ML) algorithms. The dataset comprised of 124 features, and 1700 data, post-cleaning. Of all the 124 features, 12 features were target values. We selected one of the target values (Dressler syndrome) for this study. 10% of the data was reserved for test data at the initial stage itself, and the rest was further split into 0.7:0.3 for training and validation sets. RF presented a model accuracy of 98%, which is the best of all the six algorithms. In terms of AUC, RF exhibited the highest value of 0.995. Moreover, the models were further tuned, and the results confirmed the efficacy of RF for the classification of Dressler syndrome.
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Sengupta, D., Mondal, S., Chatterjee, D., Pradhan, S., Sur, P. (2023). A Low Resource Machine Learning Approach for Prediction of Dressler Syndrome. In: Bhattacharyya, S., Koeppen, M., De, D., Piuri, V. (eds) Intelligent Systems and Human Machine Collaboration. Lecture Notes in Electrical Engineering, vol 985. Springer, Singapore. https://doi.org/10.1007/978-981-19-8477-8_6
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DOI: https://doi.org/10.1007/978-981-19-8477-8_6
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