Prediction of cyanotic and acyanotic congenital heart disease using machine learning models

doi:10.5409/wjcp.v13.i4.98472

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World Journal of Clinical Pediatrics

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2219-2808

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Clin Pediatr. Dec 9, 2024; 13(4): 98472
Published online Dec 9, 2024. doi: 10.5409/wjcp.v13.i4.98472

Prediction of cyanotic and acyanotic congenital heart disease using machine learning models

Sana Shahid, Haris Khurram, Apiradee Lim, Muhammad Farhan Shabbir, Baki Billah

Sana Shahid, Department of Statistics, Bahauddin Zakariya University, Multan 60000, Punjab, Pakistan

Haris Khurram, Apiradee Lim, Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Pattani 94000, Thailand

Haris Khurram, Department of Science and Humanities, National University of Computer and Emerging Sciences, Chiniot-Faisalabad Campus, Chiniot 35400, Punjab, Pakistan

Muhammad Farhan Shabbir, Department of Cardiology, Chaudhary Pervaiz Elhai Institute of Cardiology, Multan 60000, Punjab, Pakistan

Baki Billah, School of Public Health and Preventive Medicine, Monash University, Melbourne 3000, Victoria, Australia

Co-first authors: Sana Shahid and Haris Khurram.

Co-corresponding authors: Haris Khurram and Apiradee Lim.

Author contributions: Shahid S, Khurram H, and Lim A conceptualized and designed the research; Shahid S and Khurram H organized the dataset, performed statistical analysis and data interpretation, and wrote the first draft of the manuscript with the help of Lim A; Khurram H and Lim A played important and indispensable roles in the experimental design, data interpretation, and manuscript preparation as the co-corresponding authors; Shahid S and Khurram H made crucial and indispensable contributions towards the completion of the project and were thus qualified as the co-first authors of the paper; Lim A proofread the draft and gave valuable suggestions to improve the manuscript; Shabbir MF reviewed the final draft from a medical perspective. Billah B reviewed the final draft from a statistical perspective. All authors contributed to the manuscript revision, and read and approved the final draft.

Institutional review board statement: The study was reviewed and approved by the Advance Studies & Research Board, Bahauddin Zakariya University, Multan, Pakistan (No. 8973).

Informed consent statement: All study participants or their legal guardians gave informed verbal consent prior to study inclusion.

Conflict-of-interest statement: All authors have no conflicts of interest to disclose.

Data sharing statement: The data and code of R language are available from the corresponding author [Email: Hariskhurram2@gmail.com; haris.khurram@nu.edu.pk] upon reasonable request.

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: https://creativecommons.org/Licenses/by-nc/4.0/

Corresponding author: Haris Khurram, PhD, Assistant Professor, Postdoctoral Fellow, Department of Mathematics and Computer Science, Prince of Songkla University, 181 Village No. 6 Charoen Pradit Road, Rusamilae, Mueang Pattani District, Pattani 94000, Thailand. haris.khurram@nu.edu.pk

Received: June 27, 2024
Revised: August 28, 2024
Accepted: September 23, 2024
Published online: December 9, 2024
Processing time: 125 Days and 8.2 Hours

Core Tip

Core Tip: In this study, to identify and build the best 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 also used multivariate outlier detection methods to determine the outlier cases. The best fit model for congenital heart disease was the artificial neural network model. Children having a positive family history are at very high risk of having cyanotic and acyanotic congenital heart disease.