256 related articles for article (PubMed ID: 38467662)
1. A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction.
Zhou W; Yan Z; Zhang L
Sci Rep; 2024 Mar; 14(1):5905. PubMed ID: 38467662
[TBL] [Abstract][Full Text] [Related]
2. Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density.
Ekundayo TC; Ijabadeniyi OA; Igbinosa EO; Okoh AI
Environ Pollut; 2023 Jan; 317():120734. PubMed ID: 36455774
[TBL] [Abstract][Full Text] [Related]
3. Machine learning-based models for the prediction of breast cancer recurrence risk.
Zuo D; Yang L; Jin Y; Qi H; Liu Y; Ren L
BMC Med Inform Decis Mak; 2023 Nov; 23(1):276. PubMed ID: 38031071
[TBL] [Abstract][Full Text] [Related]
4. Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate.
Kaliappan J; Srinivasan K; Mian Qaisar S; Sundararajan K; Chang CY; C S
Front Public Health; 2021; 9():729795. PubMed ID: 34595149
[TBL] [Abstract][Full Text] [Related]
5. Seasonal prediction of daily PM
Wu Y; Lin S; Shi K; Ye Z; Fang Y
Environ Sci Pollut Res Int; 2022 Jun; 29(30):45821-45836. PubMed ID: 35150424
[TBL] [Abstract][Full Text] [Related]
6. A Risk Prediction Model for Physical Restraints Among Older Chinese Adults in Long-term Care Facilities: Machine Learning Study.
Wang J; Chen H; Wang H; Liu W; Peng D; Zhao Q; Xiao M
J Med Internet Res; 2023 Apr; 25():e43815. PubMed ID: 37023416
[TBL] [Abstract][Full Text] [Related]
7. Machine learning and deep learning methods that use omics data for metastasis prediction.
Albaradei S; Thafar M; Alsaedi A; Van Neste C; Gojobori T; Essack M; Gao X
Comput Struct Biotechnol J; 2021; 19():5008-5018. PubMed ID: 34589181
[TBL] [Abstract][Full Text] [Related]
8. Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation.
Jassim MS; Coskuner G; Zontul M
Waste Manag Res; 2022 Feb; 40(2):195-204. PubMed ID: 33818220
[TBL] [Abstract][Full Text] [Related]
9. Assessment and quantification of ovarian reserve on the basis of machine learning models.
Ding T; Ren W; Wang T; Han Y; Ma W; Wang M; Fu F; Li Y; Wang S
Front Endocrinol (Lausanne); 2023; 14():1087429. PubMed ID: 37008906
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Prediction of Undrained Shear Strength by the GMDH-Type Neural Network Using SPT-Value and Soil Physical Properties.
Kim M; Okuyucu O; Ordu E; Ordu S; Arslan Ö; Ko J
Materials (Basel); 2022 Sep; 15(18):. PubMed ID: 36143696
[TBL] [Abstract][Full Text] [Related]
12. Prediction of Outcomes After Heart Transplantation in Pediatric Patients Using National Registry Data: Evaluation of Machine Learning Approaches.
Killian MO; Tian S; Xing A; Hughes D; Gupta D; Wang X; He Z
JMIR Cardio; 2023 Jun; 7():e45352. PubMed ID: 37338974
[TBL] [Abstract][Full Text] [Related]
13. Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation.
Liu L; Bi B; Cao L; Gui M; Ju F
Front Endocrinol (Lausanne); 2024; 15():1320335. PubMed ID: 38481447
[TBL] [Abstract][Full Text] [Related]
14. Application of machine learning techniques for predicting survival in ovarian cancer.
Sorayaie Azar A; Babaei Rikan S; Naemi A; Bagherzadeh Mohasefi J; Pirnejad H; Bagherzadeh Mohasefi M; Wiil UK
BMC Med Inform Decis Mak; 2022 Dec; 22(1):345. PubMed ID: 36585641
[TBL] [Abstract][Full Text] [Related]
15. Splitting tensile strength prediction of Metakaolin concrete using machine learning techniques.
Li Q; Ren G; Wang H; Xu Q; Zhao J; Wang H; Ding Y
Sci Rep; 2023 Nov; 13(1):20102. PubMed ID: 37973915
[TBL] [Abstract][Full Text] [Related]
16. Explainable Machine Learning Model to Prediction EGFR Mutation in Lung Cancer.
Yang R; Xiong X; Wang H; Li W
Front Oncol; 2022; 12():924144. PubMed ID: 35814445
[TBL] [Abstract][Full Text] [Related]
17. A data-driven interpretable ensemble framework based on tree models for forecasting the occurrence of COVID-19 in the USA.
Zheng HL; An SY; Qiao BJ; Guan P; Huang DS; Wu W
Environ Sci Pollut Res Int; 2023 Jan; 30(5):13648-13659. PubMed ID: 36131178
[TBL] [Abstract][Full Text] [Related]
18. Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits.
Yoosefzadeh-Najafabadi M; Tulpan D; Eskandari M
PLoS One; 2021; 16(4):e0250665. PubMed ID: 33930039
[TBL] [Abstract][Full Text] [Related]
19. Prediction of Individual Gas Yields of Supercritical Water Gasification of Lignocellulosic Biomass by Machine Learning Models.
Khandelwal K; Dalai AK
Molecules; 2024 May; 29(10):. PubMed ID: 38792198
[TBL] [Abstract][Full Text] [Related]
20. Predicting case difficulty in endodontic microsurgery using machine learning algorithms.
Qu Y; Wen Y; Chen M; Guo K; Huang X; Gu L
J Dent; 2023 Jun; 133():104522. PubMed ID: 37080531
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]