Open Access

LEVERAGING MACHINE LEARNING TO IDENTIFY MATERNAL RISK FACTORS FOR CONGENITAL HEART DISEASE IN OFFSPRING

https://doi.org/10.55640/ijidml-v02i05-01

pdf

Kartik Tandon ¹ , Dr. Priya Menon ¹ ,

⁴ Department Of Computer Science And Engineering, Indian Institute Of Technology Delhi, India

⁴ Department Of Pediatrics, All India Institute Of Medical Sciences (Aiims), New Delhi, India

Abstract

Congenital Heart Defects (CHDs) represent a significant global health challenge, being the most common birth anomalies. Early identification of mothers at risk of having a child with a CHD is crucial for timely intervention, improved prenatal counseling, and better neonatal outcomes. This article explores the application of machine learning (ML) methodologies to predict the risk of CHDs in offspring based on maternal characteristics and health data. We review various ML algorithms, including traditional classifiers and advanced neural networks, that have been or could be employed for this predictive task. Key aspects of data collection, preprocessing, feature engineering, and model evaluation are discussed within the context of identifying relevant maternal risk factors. By analyzing existing literature and outlining potential experimental frameworks, this study highlights the immense potential of ML in augmenting clinical decision-making, facilitating early risk stratification, and ultimately contributing to improved maternal and child health outcomes concerning CHDs.

Keywords

Machine learning, Maternal risk factors, Congenital heart disease, Predictive modeling

References

Ali, F. S. A., Al Hammadi, S. A. A., Redouane, A., & Tariq, M. U. (2021). Prediction of congenital heart diseases in children using machine learning. Journal of Management Information and Decision Sciences, 24, 1–34. https://www.abacademies.org/articles/prediction-of-congenital-heart-diseases-in-children-using-machine-learning.pdf

Chang, J.Jr, Binuesa, F., Caneo, L. F., Turquetto, A. L. R., Arita, E. C. T. C., Barbosa, A. C., Da Silva Fernandes, A. M., Trindade, E. M., Jatene, F. B., Dossou, P., & Jatene, M. B. (2020). Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study. PLoS One, 15(9), e0238199. DOI: 10.1371/journal.pone.0238199 PMID: 32886688

Dangare, C., & Apte, S. (2012). A data mining approach for prediction of heart disease using neural networks. International Journal of Computer Engineering and Technology, 3(3). https://ssrn.com/abstract=2175569

Domyati, A., & Memon, Q. (2022). Robust detection of cardiac disease using machine learning algorithms. In Proceedings of the 5th International Conference on Control and Computer Vision (pp. 52–55). DOI: 10.1145/3561613.3561622

Desai, H., Jones, C. E., Fogel, J. L., Negrin, K. A., Slater, N. L., Morris, K., Doody, L. R., Engstler, K., Torzone, A., Smith, J., & Butler, S. C. (2023). Assessment and management of feeding difficulties for infants with complex CHD. Cardiology in the Young, 33(1), 1–10. DOI: 10.1017/S1047951122004024 PMID: 36562257

Hammoud, A., Karaki, A., Tafreshi, R., Abdulla, S., & Wahid, M. (2024). Coronary heart disease prediction: A comparative study of machine learning algorithms. Journal of Advances in Information Technology, 15(1), 27–32. DOI: 10.12720/jait.15.1.27-32

Liu, S., Liu, J., Tang, J., Ji, J., Chen, J., & Liu, C. (2009). Environmental risk factors for congenital heart disease in the Shandong peninsula, China: A hospital-based case–control study. Journal of Epidemiology, 19(3), 122–130. DOI: 10.2188/jea.JE20080039 PMID: 19398851

Lo Muzio, F. P., Rozzi, G., Rossi, S., Luciani, G. B., Foresti, R., Cabassi, A., Fassina, L., & Miragoli, M. (2021). Artificial intelligence supports decision making during open-chest surgery of rare congenital heart defects. Journal of Clinical Medicine, 10(22), 5330. DOI: 10.3390/jcm10225330 PMID: 34830612

Lopez, D., & Manogaran, G. (2018). Health data analytics using scalable logistic regression with stochastic gradient descent. International Journal of Advanced Intelligence Paradigms, 10(1/2), 118. DOI: 10.1504/IJAIP.2018.10010530

Luo, Y., Li, Z., Guo, H., Cao, H., Song, C., Guo, X., & Zhang, Y. (2017). Predicting congenital heart defects: A comparison of three data mining methods. PLoS One, 12(5), e0177811. DOI: 10.1371/journal.pone.0177811 PMID: 28542318

Meda, J. T., & Mushiri, T. (2020). Predicting congenital heart diseases using Machine learning. In Proceedings of the 2nd African International Conference on Industrial Engineering and Operations Management (pp. 1716–1725). https://www.ieomsociety.org/harare2020/papers/415.pdf

Mello, C. A., Lewis, R., Brooks-Kayal, A., Carlsen, J., Grabenstatter, H., & White, A. M. (2014). Supervised learning for the neurosurgery intensive care unit using single-layer perceptron classifiers. In Ślȩzak, D., Tan, A.-H., Peters, J. F., & Schwabe, L. (Eds.), Lecture notes in computer science: Vol. 8609. Brain informatics and health: BIH 2014 (pp. 231–241). Springer. DOI: 10.1007/978-3-319-09891-3_22

Munakata, M. (2023). Practical cutoff values of brachial–ankle pulse wave velocity to predict 10-Year CHD risk in the Japanese general population. Journal of Atherosclerosis and Thrombosis, 30(5), 437–439. DOI: 10.5551/jat.ED215 PMID: 36184559

Nordin, N., Zainol, Z., Mohd Noor, M. H., & Lai Fong, C. (2021). A comparative study of machine learning techniques for suicide attempts predictive model. Health Informatics Journal, 27(1), 1460458221989395. Advance online publication. DOI: 10.1177/1460458221989395 PMID: 33745355

Parveen, H., Rizvi, S. W. A., & Boddu, R. S. K. (2024). Enhanced knowledge based system for cardiovascular disease prediction using advanced fuzzy TOPSIS. International Journal of Intelligent Systems and Applications in Engineering, 12(11s), 570–583. https://www.ijisae.org/index.php/IJISAE/article/view/4478/3137

Peng, J., Meng, Z., Zhou, S., Zhou, Y., Wu, Y., Wang, Q., Wang, J., & Sun, K. (2019). The non‐genetic paternal factors for congenital heart defects: A systematic review and meta‐analysis. Clinical Cardiology, 42(7), 684–691. DOI: 10.1002/clc.23194 PMID: 31073996

Reddy, V. S. K., Meghana, P., Reddy, N. V. S., & Rao, B. A. (2022). Prediction on cardiovascular disease using decision tree and naïve Bayes classifiers. Journal of Physics: Conference Series, 2161(1), 012015. DOI: 10.1088/1742-6596/2161/1/012015

Rani, S., & Masood, S. (2020). Predicting congenital heart disease using machine learning techniques. Journal of Discrete Mathematical Sciences and Cryptography, 23(1), 293–303. DOI: 10.1080/09720529.2020.1721862

Qu, Y., Deng, X., Lin, S., Han, F., Chang, H. H., Ou, Y., Nie, Z., Mai, J., Wang, X., Gao, X., Wu, Y., Chen, J., Zhuang, J., Ryan, I., & Liu, X. (2022). Using innovative machine learning methods to screen and identify predictors of congenital heart diseases. Frontiers in Cardiovascular Medicine, 8, 797002. Advance online publication. DOI: 10.3389/fcvm.2021.797002 PMID: 35071361

Song, Y., Higgins, H., Guo, J., Harrison, K., Schultz, E. N., Hales, B. J., Moses, E. K., Goldblatt, J., Pachter, N., & Zhang, G. (2018). Clinical significance of circulating microRNAs as markers in detecting and predicting congenital heart defects in children. Journal of Translational Medicine, 16(1), 42. Advance online publication. DOI: 10.1186/s12967-018-1411-0 PMID: 29482591

Subhadra, K., & Vikas, B. (2019). Neural network based intelligent system for predicting heart disease. International Journal of Innovative Technology and Exploring Engineering, 8(5), 484–487. https://www.ijitee.org/wp-content/uploads/papers/v8i5/D2770028419.pdf

Truong, V. T., Nguyen, B. P., Nguyen-Vo, T., Mazur, W., Chung, E. S., Palmer, C., Tretter, J. T., Alsaied, T., Pham, V. T., Do, H. Q., Do, P. T. N., Pham, V. N., Ha, B. N., Chau, H. N., & Le, T. K. (2022). Application of machine learning in screening for congenital heart diseases using fetal echocardiography. The International Journal of Cardiovascular Imaging, 38(5), 1007–1015. DOI: 10.1007/s10554-022-02566-3 PMID: 35192082

Umm-E-Ammarah, N., Bukhari, F., Idrees, M., & Iqbal, W. (2021). Predictive analysis of congenital heart defects prior to birth. In 2021 International Conference on Robotics and Automation in Industry (ICRAI) (pp. 1–6). DOI: 10.1109/ICRAI54018.2021.9651436

Yang, H., Chen, Z., Yang, H., & Tian, M. (2023). Predicting coronary heart disease using an improved lightGBM model: Performance analysis and comparison. IEEE Access: Practical Innovations, Open Solutions, 11, 23366–23380. DOI: 10.1109/ACCESS.2023.3253885

van Hagen, I. M., & Roos-Hesselink, J. W. (2020). Pregnancy in congenital heart disease: Risk prediction and counselling. Heart (British Cardiac Society), 106(23), 1853–1861. DOI: 10.1136/heartjnl-2019-314702 PMID: 32611675

Rohit Khankhoje, "Beyond Coding: A Comprehensive Study of Low-Code, No-Code and Traditional Automation," Journal of Artificial Intelligence & Cloud Computing, vol. 1, no. 4, pp. 1-5, 2022. DOI: 10.47363/JAICC/2022(1)148.

Most read articles by the same author(s)

Mr. Madhav Sharma, Prediction of Heart Disease Using Ensemble Machine Learning Techniques , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 09 (2026): Volume 03 Issue 09
Chinedu Emmanuel Okafor, Intelligent Healthcare Systems Powered by Large Language Models: Applications, Limitations, and Emerging Research Perspectives , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 09 (2026): Volume 03 Issue 09
Bima Satria Nugraha, Professor Anindya larasati, Dr. Huỳnh Chí Dũng, Assessing The Interoperability And Semantic Readiness Of BIM And IFC Data For AI Integration In The Architecture, Engineering, And Construction Industry: A Systematic Review , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 11 (2025): Volume 02 Issue 11
Muhammad Rizky Pratama, Siti Aulia Rahma, Interpretable Predictive Analytics Approach for Robust Financial Risk Assessment and Forecasting , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 09 (2026): Volume 03 Issue 09
Dr. Nguyen Minh Anh, Dr. Tran Hoang Nam, A Scalable Multi-Tenant Framework for AI-Driven Big Data Lake Management and Processing , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 08 (2026): Volume 03 Issue 08
Elias J. Vance, Clara M. Soto, High-Frequency Data Driven Network Learning for Systemic Risk Analysis in Financial Markets , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 09 (2025): Volume 02 Issue 09
Igor Litovsky, A Systematic Review of Machine Learning Approaches For AI-Driven Fraud Detection in Loyalty Programs , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
Eko Purnomo, Rendra Alfiansyah, A Dynamic Nexus: Integrating Big Data Analytics and Distributed Computing for Real-Time Risk Management of Derivatives Portfolios , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 10 (2025): Volume 02 Issue 10
Dr. Hannah Brown, Ahmed Al-Farsi, BRIDGING DEEP LEARNING AND ADAPTIVE SYSTEMS: A PERFORMANCE STUDY ON CIFAR-10 IMAGE CLASSIFICATION , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 03 (2025): Volume 02 Issue 03
Prof. Kai O. Chen, DEVELOPING AND VALIDATING A COMPREHENSIVE DISCOURSE ANNOTATION GUIDELINE FOR LOW-RESOURCE LANGUAGES , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 10 (2025): Volume 02 Issue 10

1 2 3 4 5 6 7 > >>

International Journal of Intelligent Data and Machine Learning

LEVERAGING MACHINE LEARNING TO IDENTIFY MATERNAL RISK FACTORS FOR CONGENITAL HEART DISEASE IN OFFSPRING

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles