Abstract Objectives This research is based on Unsupervised Machine Learning Approach. We developed the automated clinical audit models using association rules . The objectives of this work is to employ decision-making support system regarding clinical audit.The audit is mandatory to all radiology diagnostic centres, as per PC-PNDT Act. Database and Methods For this study the database used is Obstetrics and gynaec radiology test reports. . The Association rules are used to analyse radiology report . BVA technique used for system testing and validation.The prediction model forcasting female fetiocite or infantasite is based on 2 subjective parameters, patient economy and education. Results The results are showing high correlation within 11 features .Out of 11 features, the 3 major features (Age, Gravida, Para) are playing vital role in skewed birth ratio of girl child in India. The most primary, feature is Age serves as the key input for prediction models, which in future can anticipation and save abortion of female fetus. Thus ML will assist streamlined PC-PNDT enforcement.
Kumbhakarna et al. (Sat,) studied this question.