TY - GEN
T1 - Comparison of LSTM and GRU in Predicting the Number of Diabetic Patients
AU - Rochman, Eka Mala Sari
AU - Miswanto,
AU - Suprajitno, Herry
AU - Rachmad, Aeri
AU - Nindyasari, Ratih
AU - Rachman, Fika Hastarita
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Diabetes
KW - GRU
KW - LSTM
KW - prediction
UR - http://www.scopus.com/inward/record.url?scp=85147090600&partnerID=8YFLogxK
U2 - 10.1109/ITIS57155.2022.10009036
DO - 10.1109/ITIS57155.2022.10009036
M3 - Conference contribution
AN - SCOPUS:85147090600
T3 - Proceeding - IEEE 8th Information Technology International Seminar, ITIS 2022
SP - 145
EP - 149
BT - Proceeding - IEEE 8th Information Technology International Seminar, ITIS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE Information Technology International Seminar, ITIS 2022
Y2 - 19 October 2022 through 21 October 2022
ER -
