Machine Learning based Approximate Query Processing for Women Health Analytics

• Published in: Procedia computer science Vol. 218; pp. 174 - 188
• Main Authors: Parvathi, A J, Gopika, H, Suresh, Jasna, Sree, Sapa Laasya, Harikumar, Sandhya
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2023
• Subjects: Approximate query processing; cervical cancer; CTG; fetal anomaly; Approximate query processing; cervical cancer; CTG; fetal anomaly
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2022.12.413

Good health and well being is one of the most essential targets of the Sustainable Development Goals (SDGs). This paper primarily focuses on Preventive and Diagnostic care of Women Health because even today, women are disadvantaged by discrimination in many societies especially in rural sectors. Two main health issues, fetal abnormality in pregnant women and cervical cancer in women are analyzed so that the doctors and patients can be given early signals to take proactive measures. As per National Health Portal of India, around 1.7 million birth defects occur in India every year. So Antenatal Care(ANC) should be given utmost importance during Pregnancy in a woman's life. Cervical cancer is another issue prevalent amongst women, especialy over the age of 30. It's critical to catch it early and eliminate any risks that come with it. Here arises the need to develop a system that can analyze and predict the aforementioned anomalies at an early stage. This work proposes approximate query processing using different dynamic machine learning algorithms to analyze and predict the abnormalities. Further, a web application is built to facilitate the stakeholders, especially doctors, to interact with the system and iteratively query the system to understand the relationships amongst the various data variables and get appropriate predictions about fetal anomaly from CTG scans as well as presence of cervical cancer from various demographic information, habits, and historic medical records. Approximate Query Processing is accomplished using Correlation analysis, linear, and logistic regression algorithms in a dynamic, interactive, and iterative manner.