Department of Computer Science and Informatics, Federal University Otuoke, PMB 126, Yenagoa, Bayelsa State, Nigeria
Research
A FRAMEWORK FOR CLASSIFICATION OF FETAL HEALTH USING DEEP LEARNING
Author(s): May Tamara Stow*
Research on child and maternal mortality risk factors has identified limited access to quality healthcare services as a significant cause of adverse outcomes. Inadequate prenatal care and skilled birth attendance increase the risk of complications during pregnancy and childbirth. A lack of essential healthcare infrastructure in rural or remote areas further exacerbates this risk. By identifying these access-related risk factors, policymakers and healthcare providers can implement strategies to improve healthcare accessibility and save countless lives. The paper presents a deep learning framework for detecting the three stages of fetal health. The dataset was highly imbalanced but solved by oversampling using the random over sampling technique. The ten most essential features were selected using a random forest classifier, and the results were used to build a convolutional neural netwo.. Read More»