Shahid Sana, Khurram Haris, Lim Apiradee, Shabbir Muhammad Farhan, Billah Baki
Department of Statistics, Bahauddin Zakariya University, Multan 60000, Punjab, Pakistan.
Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Pattani 94000, Thailand.
World J Clin Pediatr. 2024 Dec 9;13(4):98472. doi: 10.5409/wjcp.v13.i4.98472.
Congenital heart disease is most commonly seen in neonates and it is a major cause of pediatric illness and childhood morbidity and mortality.
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.
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.
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%.
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.
先天性心脏病在新生儿中最为常见，是小儿疾病以及儿童发病和死亡的主要原因。
识别并构建用于预测孕期儿童青紫型和非青紫型先天性心脏病的最佳预测模型，并确定其潜在风险因素。
数据收集于2017年12月至2019年10月期间巴基斯坦木尔坦乔德里·佩尔韦兹·埃拉希心脏病学研究所的儿科心脏病科。选取了3900名其子女被诊断为青紫型或非青紫型先天性心脏病的母亲作为样本。采用多变量异常值检测方法识别潜在异常值。比较了不同的机器学习模型，并使用模型的曲线下面积、敏感性和特异性选择最佳拟合模型。
在纳入的3900例患者中，约69.5%患有非青紫型先天性心脏病，30.5%患有青紫型先天性心脏病。与女性相比，男性患非青紫型（53.6%）和青紫型（54.5%）先天性心脏病的病例更多。母亲在孕期频繁食用更多快餐的儿童患青紫型先天性心脏病的几率高1.28倍。人工神经网络模型被选为最佳预测模型，曲线下面积为0.9012，敏感性为65.76%，特异性为97.23%。
有阳性家族史的儿童患青紫型和非青紫型先天性心脏病的风险非常高。男性风险更高，其母亲在孕期需要更多护理、优质食物和体育活动。预测青紫型和非青紫型先天性心脏病的最佳拟合模型是人工神经网络。所获得的结果和确定的最佳模型将有助于医学从业者和公共卫生科学家在关于早期诊断的知情决策过程中，并改善巴基斯坦儿童的健康状况。
