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Cited by in CrossRef
For: 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 [PMID: 39654661 DOI: 10.5409/wjcp.v13.i4.98472]
URL: https://www.wjgnet.com/2219-2808/full/v13/i4/98472.htm
Number Citing Articles
1
Sana Shahid, Haris Khurram, Muhammad Ahmed Shehzad, Muhammad Aslam. Predictive model for congenital heart disease in children of Pakistan by using structural equation modelingBMC Medical Informatics and Decision Making 2024; 24(1) doi: 10.1186/s12911-024-02774-y
2
Jingdong Qi, Fei Zhang, Xia Zhang. Global Trends, Health Inequalities, and Relationship with Socio-Demographic Index in Congenital Heart Disease: An Analysis from 1990 to 2021Congenital Heart Disease 2025; 20(3): 383 doi: 10.32604/chd.2025.064790
3
Jun Zhong, Dongmei Wei, Yihang Zhuo, Minhao Yin, Yaowen Kang, Wendi Shi. Feasibility Study and Practice of Machine Learning-Based Heart Disease Prediction2025 8th International Conference on Electronics Technology (ICET) 2025; : 492 doi: 10.1109/ICET64964.2025.11103110
4
Jason Jun Kit Yeow, Hui Na Chua, Muhammed Basheer Jasser, Bayan Issa, Ghassan Saleh Aldharhani. Meta-Ensemble Learning with Hybrid Resampling for Extreme Imbalanced Data Classification in Congenital Heart Disease Prediction2025 IEEE 15th International Conference on System Engineering and Technology (ICSET) 2025; : 336 doi: 10.1109/ICSET65917.2025.11283689
5
Shuai Peng, Mujin Zhao. Research and practice in Multi-Label Classification for Comorbidity Prediction using Machine Learning2025 International Conference on Signal Processing, Computer Networks and Communications (SPCNC) 2025; : 367 doi: 10.1109/SPCNC68200.2025.11406690