Evaluation of predisposing factors of Diabetes Mellitus post Gestational Diabetes Mellitus using Machine Learning Techniques

• Published in: 2019 IEEE Student Conference on Research and Development (SCOReD) pp. 81 - 85
• Main Authors: Krishnan, Devi R., Maddipati, Chakravarthy, Menakath, Gayathri P., Radhakrishnan, Anagha, Himavarshini, Yarrangangu, A., Aparna, Mukundan, Kaveri, Pathinarupothi, Rahul Krishnan, Alangot, Bithin, Mahankali, Sirisha
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2019
• Subjects: Gestational Diabetes Mellitus; Machine Learning; Type II Diabetes Mellitus
• ISSN: 2643-2447
• DOI: 10.1109/SCORED.2019.8896323

Diabetes Mellitus (DM) is one of the major global health challenges of the 21st century. It is a chronic disease leading to multiple complications bearing a lot of social, physical and financial impact on individuals and society. Gestational diabetes mellitus (GDM) is a type of DM that is developed in a few pregnant women although it usually reverts back to normalcy after delivery. However, it is well established that the risk in developing DM at a later stage in their lives increases with GDM. Very few works done in this area explore the possibility of using prognostic Machine Learning algorithms to predict occurrence of DM after GDM. In this paper, we conduct a methodical review of current practices, and then analyze GDM data from our University hospital to identify predisposing factors that could be used as inputs to different ML techniques.