University of the Punjab, Pakistan.
Prince Sultan University, Saudi Arabia.
Health Informatics J. 2020 Dec;26(4):2568-2585. doi: 10.1177/1460458220911789. Epub 2020 Apr 14.
In developing countries like Pakistan, cleft surgery is expensive for families, and the child also experiences much pain. In this article, we propose a machine learning-based solution to avoid cleft in the mother's womb. The possibility of cleft lip and palate in embryos can be predicted before birth by using the proposed solution. We collected 1000 pregnant female samples from three different hospitals in Lahore, Punjab. A questionnaire has been designed to obtain a variety of data, such as gender, parenting, family history of cleft, the order of birth, the number of children, midwives counseling, miscarriage history, parent smoking, and physician visits. Different cleaning, scaling, and feature selection methods have been applied to the data collected. After selecting the best features from the cleft data, various machine learning algorithms were used, including random forest, -nearest neighbor, decision tree, support vector machine, and multilayer perceptron. In our implementation, multilayer perceptron is a deep neural network, which yields excellent results for the cleft dataset compared to the other methods. We achieved 92.6% accuracy on test data based on the multilayer perceptron model. Our promising results of predictions would help to fight future clefts for children who would have cleft.
在巴基斯坦等发展中国家，唇腭裂手术对家庭来说费用昂贵，孩子也会经历很多痛苦。在本文中，我们提出了一种基于机器学习的解决方案，以避免孩子在母亲子宫中出现唇腭裂。通过使用所提出的解决方案，可以在婴儿出生前预测胚胎出现唇腭裂的可能性。我们从旁遮普省拉合尔的三家不同医院收集了 1000 名孕妇样本。设计了一份问卷，以获取各种数据，如性别、育儿、唇腭裂家族史、出生顺序、孩子数量、助产士咨询、流产史、父母吸烟和医生就诊情况。我们对收集到的数据应用了不同的数据清理、缩放和特征选择方法。在从唇腭裂数据中选择最佳特征后，我们使用了各种机器学习算法，包括随机森林、-近邻、决策树、支持向量机和多层感知机。在我们的实现中，多层感知机是一种深度神经网络，与其他方法相比，它在唇腭裂数据集上的表现非常出色。我们基于多层感知机模型在测试数据上实现了 92.6%的准确率。我们对预测结果充满信心，这将有助于对抗未来那些可能患有唇腭裂的孩子的唇腭裂问题。
