Publication Name	World Journal of Clinical Pediatrics
Manuscript ID	98472
Country	Thailand

Category	Cardiac & Cardiovascular Systems
Manuscript Type	Retrospective Study
Article Title	Prediction of cyanotic and acyanotic congenital heart disease using machine learning models
Manuscript Source	Invited Manuscript
All Author List	Sana Shahid, Haris Khurram, Apiradee Lim, Muhammad Farhan Shabbir and Baki Billah
Funding Agency and Grant Number
Corresponding Author	Haris Khurram, PhD, Assistant Professor, Postdoctoral Fellow, Department of Mathematics and Computer Science, Prince of Songkla University, 181 หมู่ที่ 6 Charoen Pradit Rd, Rusamilae, Mueang Pattani District, Pattani 94000, Thailand. haris.khurram@nu.edu.pk
Key Words	Congenital heart disease; Cyanotic heart disease; Acyanotic heart disease; Logistic Regression model; Artificial Neural network
Core Tip	In this study, to identify and build the best Predictive model for predicting cyanotic and acyanotic congenital heart disease in children during pregnancy and identify their risk factors, we employed machine learning models and compared their performance to choose the best one. We have also used multivariate outlier detection methods to and determine the outlier cases. The best fit model for congenital heart disease was the artificial neural network model. Children with having positive family history are very high at risk of having cyanotic and acyanotic congenital heart disease.
Citation	<p>Shahid S, Khurram H, Lim A, Shabbir MF, Billah B. Prediction of cyanotic and acyanotic congenital heart disease using machine learning models. <i>World J Clin Pediatr</i> 2024; 13(4): 98472</p>

Received	2024-06-27 03:32
Peer-Review Started	2024-06-27 03:32
To Make the First Decision
Return for Revision	2024-08-04 16:44
Revised	2024-08-28 02:21
Second Decision	2024-09-23 02:40
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-09-23 11:16
Articles in Press	2024-09-23 11:16
Publication Fee Transferred
Edit the Manuscript by Language Editor	2024-09-27 01:57
Typeset the Manuscript	2024-10-10 09:16

ISSN	2219-2808 (online)
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