Performance Assessment of Supervised Learning Techniques for Caesarean Rate Prediction

5 Pages Posted: 4 Feb 2020 Last revised: 27 Feb 2021

See all articles by Rydhm Beri

Rydhm Beri

Lovely Professional University

Mithilesh Kr. Dubey

Lovely Professional University

Anita Gehlot

Lovely Professional University

Rajesh RAJ

Lovely Professional University, Jalandhar, Punjab

Date Written: January 10, 2020

Abstract

Machine learning techniques automate the decision making process. These techniques, when applied to the healthcare industry, improve the health conditions of the patient and offers a reduction of the cost of healthcare services. The current study used machine learning classification techniques to predict the chances of caesarean. The dataset used in the study includes 80 instances of pregnant women with six health attributes. In this study, 14 classification techniques are applied to the dataset using ten cross-fold validation training methods. The results obtained from the prediction conclude that the Naïve Bayes algorithm provides more accurate results as compared to other models applied onto the dataset.

Keywords: Machine Learning, Classification Techniques, C-Section Prediction, Caesarean Prediction

Suggested Citation

Beri, Rydhm and Dubey, Mithilesh Kr. and Gehlot, Anita and RAJ, Rajesh, Performance Assessment of Supervised Learning Techniques for Caesarean Rate Prediction (January 10, 2020). Available at SSRN: https://ssrn.com/abstract=3517430 or http://dx.doi.org/10.2139/ssrn.3517430

Rydhm Beri (Contact Author)

Lovely Professional University ( email )

Lovely Professional University
Phagwara, 144401
India

Mithilesh Kr. Dubey

Lovely Professional University ( email )

Anita Gehlot

Lovely Professional University ( email )

Jalandhar Delhi GT Road
Phagwara, Punjab 144411
India

Rajesh RAJ

Lovely Professional University, Jalandhar, Punjab ( email )

Jalandhar Delhi GT Road
Phagwara, Punjab 144411
India
9837043685 (Phone)
248007 (Fax)

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