Prognostication of the Termination of Contraceptive Utilization: An In-depth Analysis

This research paper presents a comprehensive analysis of contraceptive discontinuation and its influencing factors. Using a dataset encompassing diverse socio-economic and demographic attributes, various machine learning algorithms were employed to develop a predictive model for identifying contraceptive discontinuation risk. The research paper aims to aid healthcare providers and policymakers in early intervention strategies and resource allocation. Association rule mining techniques especially Apriori Algorithm were applied to unveil intricate relationships among attributes affecting discontinuation. The findings contribute to improved family planning services, empowering women to make informed decisions. This research demonstrates the potential of data-driven insights to address reproductive health challenges effectively.