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

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
Publish the Manuscript Online	2024-10-30 10:50

ISSN	2219-2808 (online)
Open Access	This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
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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
ORCID	Author(s) ORCID Number Haris Khurram http://orcid.org/0000-0003-1814-4742
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.
Publish Date	2024-10-30 10:50
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>
URL	https://www.wjgnet.com/2219-2808/full/v13/i4/98472.htm
DOI	https://dx.doi.org/10.5409/wjcp.v13.i4.98472

Full Article (PDF)	WJCP-13-98472-with-cover.pdf
Manuscript File	98472_Auto_Edited_134807.docx
Answering Reviewers	98472-answering-reviewers.pdf
Audio Core Tip	98472-audio.mp3
Biostatistics Review Certificate	98472-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	98472-conflict-of-interest-statement.pdf
Copyright License Agreement	98472-copyright-assignment.pdf
Signed Informed Consent Form(s) or Document(s)	98472-informed-consent-statement.pdf
Institutional Review Board Approval Form or Document	98472-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate	98472-non-native-speakers.pdf
Supplementary Material	98472-supplementary-material.pdf
Peer-review Report	98472-peer-reviews.pdf
Scientific Misconduct Check	98472-scientific-misconduct-check.png
Scientific Editor Work List	98472-scientific-editor-work-list.pdf
CrossCheck Report	98472-crosscheck-report.pdf
CrossCheck Report	98472-crosscheck-report.png