Machine Learning Based Recommendation Systems for the Mode of Childbirth

Abstract

Machine learning method gives a learning technique that can be applied to extract information from data. Lots of researches are being conducted that involves machine learning techniques for medical diagnosis, prediction and treatment. The goal of this study is to perform several machine learning actions for finding the appropriate mode of birth (cesarean or normal) to minimize maternal mortality rate. To generate a computer-aided decision for selecting between the most common way of baby birth, C-section and vaginal birth, we have used supervised machine learning to train our classification model. A dataset consists of the information of 13,527 delivery patients has been collected from Tarail Upazilla Health complex, Bangladesh. We have implemented nine machine learning classifier algorithms over the whole datasets and compared the performances of all those proposed techniques. The computer recommended mode of baby delivery suggested by the most convincing method named “impact learning,” showed an accuracy of 0.89089172 with the F1 value of 0.877871741.