Identifying At-risk Patients for Congenital Heart Disease Using Integrated Predictive Models and Fuzzy Clustering Analysis: A Cross-sectional Study

Overview

Journal Heliyon

Specialty Social Sciences

Date 2024 Nov 5

PMID 39498045

Authors

Amirreza Salehi

Majid Khedmati

Affiliations

Soon will be listed here.

Abstract

Congenital heart disease (CHD) remains a significant global health concern, affecting approximately 1 % of newborns worldwide. While its accurate causes often remain elusive, a combination of genetic and environmental factors is implicated. In this cross-sectional study, we propose a comprehensive prediction framework leveraging Machine Learning (ML) and Multi-Attribute Decision Making (MADM) techniques to enhance CHD diagnostics and forecasting. Our framework integrates supervised and unsupervised learning methodologies to remove data noise and address imbalanced datasets effectively. Through the utilization of imbalance ensemble methods and clustering algorithms such as K-means, we enhance predictive accuracy, particularly in non-clinical datasets where imbalances are prevalent. Our results demonstrate an improvement of 8 % in recall compared to existing literature, showcasing the efficacy of our approach. Moreover, our framework identifies clusters of patients at the highest risk using MADM techniques, providing insights into susceptibility to CHD. Fuzzy clustering techniques further assess the degree of risk for individuals within each cluster, enabling personalized risk evaluation. Importantly, our analysis reveals that unhealthy lifestyle factors, annual per capita income, nutrition, and folic acid supplementation emerge as crucial predictors of CHD occurrences. Additionally, environmental risk factors and maternal illnesses significantly contribute to the predictive model. These findings underscore the multifactorial nature of CHD development, emphasizing the importance of considering socioeconomic and lifestyle factors alongside medical variables in CHD risk assessment and prevention strategies. Our proposed framework offers a promising avenue for early identification and intervention, potentially mitigating the burden of CHD on affected individuals and healthcare systems globally.

References

Cao H, Wei X, Guo X, Song C, Luo Y, Cui Y . Screening high-risk clusters for developing birth defects in mothers in Shanxi Province, China: application of latent class cluster analysis. BMC Pregnancy Childbirth. 2015; 15:343. PMC: 4687365. DOI: 10.1186/s12884-015-0783-x. View

Shi H, Yang D, Tang K, Hu C, Li L, Zhang L . Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease. Clin Nutr. 2021; 41(1):202-210. DOI: 10.1016/j.clnu.2021.11.006. View

Reddy C, Van den Eynde J, Kutty S . Artificial intelligence in perinatal diagnosis and management of congenital heart disease. Semin Perinatol. 2022; 46(4):151588. DOI: 10.1016/j.semperi.2022.151588. View

Miller R, Bednarski B, Pieszko K, Kwiecinski J, Williams M, Shanbhag A . Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study. EBioMedicine. 2024; 99:104930. PMC: 10794922. DOI: 10.1016/j.ebiom.2023.104930. View

Liu X, Wu J, Zhou Z . Exploratory undersampling for class-imbalance learning. IEEE Trans Syst Man Cybern B Cybern. 2008; 39(2):539-50. DOI: 10.1109/TSMCB.2008.2007853. View

Xu X, Jia Q, Yuan H, Qiu H, Dong Y, Xie W . A clinically applicable AI system for diagnosis of congenital heart diseases based on computed tomography images. Med Image Anal. 2023; 90:102953. DOI: 10.1016/j.media.2023.102953. View

Luo D, Zheng X, Yang Z, Li H, Fei H, Zhang C . Machine learning for clustering and postclosure outcome of adult CHD-PAH patients with borderline hemodynamics. J Heart Lung Transplant. 2023; 42(9):1286-1297. DOI: 10.1016/j.healun.2023.05.003. View

Jenkins K, Correa A, Feinstein J, Botto L, Britt A, Daniels S . Noninherited risk factors and congenital cardiovascular defects: current knowledge: a scientific statement from the American Heart Association Council on Cardiovascular Disease in the Young: endorsed by the American Academy of Pediatrics. Circulation. 2007; 115(23):2995-3014. DOI: 10.1161/CIRCULATIONAHA.106.183216. View

Richmond S, Wren C . Early diagnosis of congenital heart disease. Semin Neonatol. 2001; 6(1):27-35. DOI: 10.1053/siny.2000.0028. View

10.

Han P, Jiang L, Cheng J, Shi K, Huang S, Jiang Y . Artificial intelligence-assisted diagnosis of congenital heart disease and associated pulmonary arterial hypertension from chest radiographs: A multi-reader multi-case study. Eur J Radiol. 2023; 171:111277. DOI: 10.1016/j.ejrad.2023.111277. View

11.

Boneva R, Botto L, Moore C, Yang Q, Correa A, Erickson J . Mortality associated with congenital heart defects in the United States: trends and racial disparities, 1979-1997. Circulation. 2001; 103(19):2376-81. DOI: 10.1161/01.cir.103.19.2376. View

12.

Davies D, Bouldin D . A cluster separation measure. IEEE Trans Pattern Anal Mach Intell. 2011; 1(2):224-7. View

13.

Luo Y, Li Z, Guo H, Cao H, Song C, Guo X . Predicting congenital heart defects: A comparison of three data mining methods. PLoS One. 2017; 12(5):e0177811. PMC: 5443514. DOI: 10.1371/journal.pone.0177811. View

14.

Moons P, Van Deyk K, De Geest S, Gewillig M, Budts W . Is the severity of congenital heart disease associated with the quality of life and perceived health of adult patients?. Heart. 2005; 91(9):1193-8. PMC: 1769070. DOI: 10.1136/hrt.2004.042234. View

15.

Kaur I, Ahmad T . A cluster-based ensemble approach for congenital heart disease prediction. Comput Methods Programs Biomed. 2023; 243:107922. DOI: 10.1016/j.cmpb.2023.107922. View

16.

Patel S, Burns T . Nongenetic risk factors and congenital heart defects. Pediatr Cardiol. 2013; 34(7):1535-55. DOI: 10.1007/s00246-013-0775-4. View

17.

Chang Junior J, Caneo L, Turquetto A, Amato L, Arita E, Fernandes A . Predictors of in-ICU length of stay among congenital heart defect patients using artificial intelligence model: A pilot study. Heliyon. 2024; 10(4):e25406. PMC: 10869777. DOI: 10.1016/j.heliyon.2024.e25406. View

18.

Griffeth E, Stephens E, Dearani J, Shreve J, OSullivan D, Egbe A . Impact of heart failure on reoperation in adult congenital heart disease: An innovative machine learning model. J Thorac Cardiovasc Surg. 2023; 167(6):2215-2225.e1. PMC: 10972775. DOI: 10.1016/j.jtcvs.2023.09.045. View

19.

Song Y, Song W, Yu X, Afgan M, Liu J, Gu W . Improvement of sample discrimination using laser-induced breakdown spectroscopy with multiple-setting spectra. Anal Chim Acta. 2021; 1184:339053. DOI: 10.1016/j.aca.2021.339053. View

20.

He Q, Liu Y, Dou Z, Ma K, Li S . Congenital heart diseases with airway stenosis: a predictive nomogram to risk-stratify patients without airway intervention. BMC Pediatr. 2023; 23(1):351. PMC: 10337114. DOI: 10.1186/s12887-023-04160-5. View