Using HMM, Association Rule Mining and Ensemble Methods with the Application of Latent Factor Model to Detect Gestational Diabetes Mellitus

Jayashree S. Shetty, Nisha P. Shetty*, Vedant Rishi Das, Vaibhav, Diana Olivia

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Gestational diabetes mellitus (GDM) is a condition often seen during pregnancies in which a hormone made by the placenta prevents the body from using insulin effectively. Women with GDM are at an increased risk of complications during pregnancy and during delivery. The offspring and the mother are also at an increased risk of getting diabetes in the future. Therefore, careful screening is necessary to avoid further complications. The objective of this research is to facilitate proper prediction of the presence of GDM in women so that timely intervention can help prevent future adversities. Multiple machine learning algorithms with data analysis methods are employed to investigate the probability of GDM and reach an optimal solution. The methodology makes use of the latent factor model and stochastic gradient descent to account for the missing data. Information entropy is used to calculate the amount of information each variable presents. The final classification is done and compared using three methods. These include ensemble method, hidden Markov model, and association analysis. Experiments reveal that the ensemble method involving decision trees, k-nearest neighbors, and logistic regression with weighted averaging delivers promising performance. Test data accuracy of 80% was recorded on the ensemble method.

Original languageEnglish
Title of host publicationEmerging Technologies in Data Mining and Information Security - Proceedings of IEMIS 2022
EditorsParamartha Dutta, Satyajit Chakrabarti, Abhishek Bhattacharya, Soumi Dutta, Vincenzo Piuri
PublisherSpringer Science and Business Media Deutschland GmbH
Pages215-223
Number of pages9
ISBN (Print)9789811941924
DOIs
Publication statusPublished - 2023
Event3rd International Conference on Emerging Technologies in Data Mining and Information Security, IEMIS 2022 - Kolkata, India
Duration: 23-02-202225-02-2022

Publication series

NameLecture Notes in Networks and Systems
Volume491
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference3rd International Conference on Emerging Technologies in Data Mining and Information Security, IEMIS 2022
Country/TerritoryIndia
CityKolkata
Period23-02-2225-02-22

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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