Cesarean Section Classification Using Machine Learning With Feature Selection, Data Balancing, and Explainability

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...

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Bibliographic Details
Main Authors: Nahid Sultan, Mahmudul Hasan, Md. Ferdous Wahid, Hasi Saha, Ahsan Habib
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Cesarean section
feature selection
data balancing
machine learning
explainable AI
Online Access:https://ieeexplore.ieee.org/document/10210557/

Internet

https://ieeexplore.ieee.org/document/10210557/

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