Early Detection of Birth Defects Using Machine Learning

Abstract

Finding birth flaws early is important for getting help right away. Machine learning (ML) methods promise recognition processes more accurate and faster. Convolutional neural networks (CNNs) and gradient boosting machines (GBMs) are used to find complicated patterns that could point to early birth problems. A variety of datasets like fetal ultrasound pictures, genetic data, mother health records, and demographic data are used in this study. The ML models are taught on labelled data, which includes accurate diagnoses of birth defects, and they are checked for accuracy using strict cross-validation methods. The proposed method is not only focused on getting at classifying things, but also on figuring out learning of biological processes that cause birth problems. The easy-to-use interface can be designed for healthcare professionals. Initial results show that the proposed method can correctly find neural tube defects and fetal heart and chromosomal abnormalities. This gives healthcare workers the ability to offer quick guidance and assistance to pregnant parents.