Shahid S, Khurram H, Lim A, Shabbir MF, Billah B. Prediction of cyanotic and acyanotic congenital heart disease using machine learning models. World J Clin Pediatr 2024; 13(4): 98472 [DOI: 10.5409/wjcp.v13.i4.98472]
Corresponding Author of This Article
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
Research Domain of This Article
Cardiac & Cardiovascular Systems
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
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.
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
Abstract
BACKGROUND
Congenital heart disease is most commonly seen in neonates and it is a major cause of pediatric illness and childhood morbidity and mortality.
AIM
To identify and build the best predictive model for predicting cyanotic and acyanotic congenital heart disease in children during pregnancy and identify their potential risk factors.
METHODS
The data were collected from the Pediatric Cardiology Department at Chaudhry Pervaiz Elahi Institute of Cardiology Multan, Pakistan from December 2017 to October 2019. A sample of 3900 mothers whose children were diagnosed with cyanotic or acyanotic congenital heart disease was taken. Multivariate outlier detection methods were used to identify the potential outliers. Different machine learning models were compared, and the best-fitted model was selected using the area under the curve, sensitivity, and specificity of the models.
RESULTS
Out of 3900 patients included, about 69.5% had acyanotic and 30.5% had cyanotic congenital heart disease. Males had more cases of acyanotic (53.6%) and cyanotic (54.5%) congenital heart disease as compared to females. The odds of having cyanotic was 1.28 times higher for children whose mothers used more fast food frequently during pregnancy. The artificial neural network model was selected as the best predictive model with an area under the curve of 0.9012, sensitivity of 65.76%, and specificity of 97.23%.
CONCLUSION
Children having a positive family history are at very high risk of having cyanotic and acyanotic congenital heart disease. Males are more at risk and their mothers need more care, good food, and physical activity during pregnancy. The best-fitted model for predicting cyanotic and acyanotic congenital heart disease is the artificial neural network. The results obtained and the best model identified will be useful for medical practitioners and public health scientists for an informed decision-making process about the earlier diagnosis and improve the health condition of children in Pakistan.
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.
