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- W4379408897 abstract "Cardiac diseases are diseases that affect people across the globe, and cardiac failure occurs without any warning. Identification of cardiac diseases at an early stage becomes a challenge for researchers in the health domain. Machine learning frameworks and algorithms are effectively used in the current medical field to predict and classify various diseases accurately. In this paper, we explore the traditional supervised machine learning techniques and algorithms and their cardiac disease classification accuracy. We further investigate the feature extraction technique kernel principal component analysis with a pipelined framework. The proposed framework overcame the issue of overfitting and increased the prediction accuracy most effectively. Random forest (RF) produced the perfect result, and the extreme gradient boost technique achieved an accuracy of 99.02%. Other boosting classifiers, gradient boosting and light gradient boosting machine, produced an accuracy of 94.16% and 98.38%, respectively." @default.
- W4379408897 created "2023-06-06" @default.
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- W4379408897 date "2023-01-01" @default.
- W4379408897 modified "2023-09-30" @default.
- W4379408897 title "A Pipelined Framework for the Prediction of Cardiac Disease with Dimensionality Reduction" @default.
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- W4379408897 doi "https://doi.org/10.1007/978-3-031-23683-9_21" @default.
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