204 related articles for article (PubMed ID: 36972270)
1. A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh.
Noorunnahar M; Chowdhury AH; Mila FA
PLoS One; 2023; 18(3):e0283452. PubMed ID: 36972270
[TBL] [Abstract][Full Text] [Related]
2. Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh.
Rahman MS; Chowdhury AH; Amrin M
PLOS Glob Public Health; 2022; 2(5):e0000495. PubMed ID: 36962227
[TBL] [Abstract][Full Text] [Related]
3. Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model.
Lv CX; An SY; Qiao BJ; Wu W
BMC Infect Dis; 2021 Aug; 21(1):839. PubMed ID: 34412581
[TBL] [Abstract][Full Text] [Related]
4. Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study.
Alim M; Ye GH; Guan P; Huang DS; Zhou BS; Wu W
BMJ Open; 2020 Dec; 10(12):e039676. PubMed ID: 33293308
[TBL] [Abstract][Full Text] [Related]
5. Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study.
Fang ZG; Yang SQ; Lv CX; An SY; Wu W
BMJ Open; 2022 Jul; 12(7):e056685. PubMed ID: 35777884
[TBL] [Abstract][Full Text] [Related]
6. Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA).
ArunKumar KE; Kalaga DV; Sai Kumar CM; Chilkoor G; Kawaji M; Brenza TM
Appl Soft Comput; 2021 May; 103():107161. PubMed ID: 33584158
[TBL] [Abstract][Full Text] [Related]
7. Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study.
Wang YW; Shen ZZ; Jiang Y
BMJ Open; 2019 Jun; 9(6):e025773. PubMed ID: 31209084
[TBL] [Abstract][Full Text] [Related]
8. Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models.
Adeyinka DA; Muhajarine N
BMC Med Res Methodol; 2020 Dec; 20(1):292. PubMed ID: 33267817
[TBL] [Abstract][Full Text] [Related]
9. A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study.
Yu CS; Chang SS; Chang TH; Wu JL; Lin YJ; Chien HF; Chen RJ
J Med Internet Res; 2021 May; 23(5):e27806. PubMed ID: 33900932
[TBL] [Abstract][Full Text] [Related]
10. Time series analysis of human brucellosis in mainland China by using Elman and Jordan recurrent neural networks.
Wu W; An SY; Guan P; Huang DS; Zhou BS
BMC Infect Dis; 2019 May; 19(1):414. PubMed ID: 31088391
[TBL] [Abstract][Full Text] [Related]
11. Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India.
Paul RK; Yeasin M; Kumar P; Kumar P; Balasubramanian M; Roy HS; Paul AK; Gupta A
PLoS One; 2022; 17(7):e0270553. PubMed ID: 35793366
[TBL] [Abstract][Full Text] [Related]
12. Predicting Readmission Charges Billed by Hospitals: Machine Learning Approach.
Gopukumar D; Ghoshal A; Zhao H
JMIR Med Inform; 2022 Aug; 10(8):e37578. PubMed ID: 35896038
[TBL] [Abstract][Full Text] [Related]
13. Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China.
Lou HR; Wang X; Gao Y; Zeng Q
BMC Public Health; 2022 Nov; 22(1):2167. PubMed ID: 36434563
[TBL] [Abstract][Full Text] [Related]
14. Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population.
Li Z; Wang Z; Song H; Liu Q; He B; Shi P; Ji Y; Xu D; Wang J
Infect Drug Resist; 2019; 12():1011-1020. PubMed ID: 31118707
[No Abstract] [Full Text] [Related]
15. Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans.
Zhou L; Xia J; Yu L; Wang Y; Shi Y; Cai S; Nie S
Int J Environ Res Public Health; 2016 Mar; 13(4):355. PubMed ID: 27023573
[TBL] [Abstract][Full Text] [Related]
16. Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis.
Zhai M; Li W; Tie P; Wang X; Xie T; Ren H; Zhang Z; Song W; Quan D; Li M; Chen L; Qiu L
BMC Infect Dis; 2021 Mar; 21(1):280. PubMed ID: 33740904
[TBL] [Abstract][Full Text] [Related]
17. Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach.
Ye GH; Alim M; Guan P; Huang DS; Zhou BS; Wu W
PLoS One; 2021; 16(3):e0248597. PubMed ID: 33725011
[TBL] [Abstract][Full Text] [Related]
18. Prediction of hepatitis E using machine learning models.
Guo Y; Feng Y; Qu F; Zhang L; Yan B; Lv J
PLoS One; 2020; 15(9):e0237750. PubMed ID: 32941452
[TBL] [Abstract][Full Text] [Related]
19. Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China.
Zhang R; Song H; Chen Q; Wang Y; Wang S; Li Y
PLoS One; 2022; 17(1):e0262009. PubMed ID: 35030203
[TBL] [Abstract][Full Text] [Related]
20. Time series model for forecasting the number of new admission inpatients.
Zhou L; Zhao P; Wu D; Cheng C; Huang H
BMC Med Inform Decis Mak; 2018 Jun; 18(1):39. PubMed ID: 29907102
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
