Cesarean Section Classification Using Machine Learning With Feature Selection, Data Balancing, and Explainability
Disease samples are naturally fewer than healthy samples which introduces bias in the training of machine learning (ML) models. Current study focuses in learning discriminating patterns between cesarean and non-cesarean phenomena based on a dataset consisting of 161 features of total 692 cesarean an...
Main Authors: | Nahid Sultan, Mahmudul Hasan, Md. Ferdous Wahid, Hasi Saha, Ahsan Habib |
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Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
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Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10210557/ |
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