Publication Type : Conference Paper
Thematic Areas : Wireless Network and Application
Publisher : 2019 IEEE Student Conference on Research and Development (SCOReD).
Source : 2019 IEEE Student Conference on Research and Development (SCOReD), pp. 81-85. IEEE, 2019
Url : https://ieeexplore.ieee.org/document/8896323
Campus : Amritapuri
School : School of Engineering
Center : Amrita Center for Wireless Networks and Applications (AmritaWNA)
Department : Wireless Networks and Applications (AWNA)
Verified : Yes
Year : 2019
Abstract : 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.
Cite this Research Publication : Krishnan, Devi R., Gayathri P. Menakath, Anagha Radhakrishnan, YarranganguHimavarshini, A. Aparna, Kaveri Mukundan, Rahul Krishnan Pathinarupothi, BithinAlangot, Sirisha Mahankali, and Chakravarthy Maddipati. "Evaluation of predisposing factors of Diabetes Mellitus post Gestational Diabetes Mellitus using Machine Learning Techniques." In 2019 IEEE Student Conference on Research and Development (SCOReD), pp. 81-85. IEEE, 2019.