Comparison of LSTM and GRU in Predicting the Number of Diabetic Patients

Eka Mala Sari Rochman, Miswanto, Herry Suprajitno, Aeri Rachmad, Ratih Nindyasari, Fika Hastarita Rachman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Diabetes is one of the chronic diseases that many people have. This diabetes disease experienced a significant increase during the pandemic, which could cause numerous deaths. One way to help hospitals prevent too many diabetic patients is to predict the number of diabetic patients. We used the LSTM (Long Short-Term Memory) method to predict diabetic patients. The study was conducted using patient data from the Modopuro Health Center, Mojokerto Regency. The prediction process manually calculates the data, then looks for the correlation of the data according to the LSTM method, namely identifying the autocorrelation coefficients at two to three different time lags. The data observed is daily from January 2, 2021, to April 20, 2022, with as many as 345 data. From the calculation results, the RMSE value is 3.184, while the GRU produces an RMSE of 1.727. It concluded that the GRU could better predict the number of visits of diabetic patients in internal medicine polyclinics.

Original languageEnglish
Title of host publicationProceeding - IEEE 8th Information Technology International Seminar, ITIS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages145-149
Number of pages5
ISBN (Electronic)9798350398199
DOIs
Publication statusPublished - 2022
Event8th IEEE Information Technology International Seminar, ITIS 2022 - Surabaya, Indonesia
Duration: 19 Oct 202221 Oct 2022

Publication series

NameProceeding - IEEE 8th Information Technology International Seminar, ITIS 2022

Conference

Conference8th IEEE Information Technology International Seminar, ITIS 2022
Country/TerritoryIndonesia
CitySurabaya
Period19/10/2221/10/22

Keywords

  • Diabetes
  • GRU
  • LSTM
  • prediction

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