Incremental Machine Learning Model for Fetal Health Risk Prediction
Vidyalekshmi Chandrika, Simi Surendran
Abstract
One of the significant health problems around the globe is associated with neonatal mortality or morbidity and disability in later life. The primary cause of neonatal death is preterm labor, which accounts for less than a half percent of all deaths among children under five years. Continuous monitoring and risk prediction could help provide medical assistance to the pregnant woman at the right time, substantially reducing neonatal mortality. We conducted a detailed data analysis and comparative study on various machine learning models on the cardiotocography dataset to conclude on a better accuracy of preterm birth prediction. This paper proposes an incremental learning approach to predict preterm labor risk. The response time for the medical aid can be significantly reduced using this incremental edge learning.