228 related articles for article (PubMed ID: 33489117)
1. Comparing supervised and semi-supervised Machine Learning Models on Diagnosing Breast Cancer.
Al-Azzam N; Shatnawi I
Ann Med Surg (Lond); 2021 Feb; 62():53-64. PubMed ID: 33489117
[TBL] [Abstract][Full Text] [Related]
2. Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.
Wu Z; Zhu M; Kang Y; Leung EL; Lei T; Shen C; Jiang D; Wang Z; Cao D; Hou T
Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33313673
[TBL] [Abstract][Full Text] [Related]
3. Comprehensive study of semi-supervised learning for DNA methylation-based supervised classification of central nervous system tumors.
Tran QT; Alom MZ; Orr BA
BMC Bioinformatics; 2022 Jun; 23(1):223. PubMed ID: 35676649
[TBL] [Abstract][Full Text] [Related]
4. Prediction and Diagnosis of Breast Cancer Using Machine and Modern Deep Learning Models.
Devi S; Kaul Ghanekar R; Pande JA; Dumbre D; Chavan R; Gupta H
Asian Pac J Cancer Prev; 2024 Mar; 25(3):1077-1085. PubMed ID: 38546090
[TBL] [Abstract][Full Text] [Related]
5. Application of supervised machine learning algorithms for classification and prediction of type-2 diabetes disease status in Afar regional state, Northeastern Ethiopia 2021.
Ebrahim OA; Derbew G
Sci Rep; 2023 May; 13(1):7779. PubMed ID: 37179444
[TBL] [Abstract][Full Text] [Related]
6. Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer.
Ozcan I; Aydin H; Cetinkaya A
Asian Pac J Cancer Prev; 2022 Oct; 23(10):3287-3297. PubMed ID: 36308351
[TBL] [Abstract][Full Text] [Related]
7. [Construction of a predictive model for in-hospital mortality of sepsis patients in intensive care unit based on machine learning].
Zhu M; Hu C; He Y; Qian Y; Tang S; Hu Q; Hao C
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Jul; 35(7):696-701. PubMed ID: 37545445
[TBL] [Abstract][Full Text] [Related]
8. Application of supervised machine learning algorithms in the classification of sagittal gait patterns of cerebral palsy children with spastic diplegia.
Zhang Y; Ma Y
Comput Biol Med; 2019 Mar; 106():33-39. PubMed ID: 30665140
[TBL] [Abstract][Full Text] [Related]
9. Which supervised machine learning algorithm can best predict achievement of minimum clinically important difference in neck pain after surgery in patients with cervical myelopathy? A QOD study.
Park C; Mummaneni PV; Gottfried ON; Shaffrey CI; Tang AJ; Bisson EF; Asher AL; Coric D; Potts EA; Foley KT; Wang MY; Fu KM; Virk MS; Knightly JJ; Meyer S; Park P; Upadhyaya C; Shaffrey ME; Buchholz AL; Tumialán LM; Turner JD; Sherrod BA; Agarwal N; Chou D; Haid RW; Bydon M; Chan AK
Neurosurg Focus; 2023 Jun; 54(6):E5. PubMed ID: 37283449
[TBL] [Abstract][Full Text] [Related]
10. Applications of Machine Learning to Diagnosis of Parkinson's Disease.
Lai H; Li XY; Xu F; Zhu J; Li X; Song Y; Wang X; Wang Z; Wang C
Brain Sci; 2023 Nov; 13(11):. PubMed ID: 38002506
[TBL] [Abstract][Full Text] [Related]
11. Comparison of Supervised Machine Learning Algorithms for Classifying of Home Discharge Possibility in Convalescent Stroke Patients: A Secondary Analysis.
Imura T; Toda H; Iwamoto Y; Inagawa T; Imada N; Tanaka R; Inoue Y; Araki H; Araki O
J Stroke Cerebrovasc Dis; 2021 Oct; 30(10):106011. PubMed ID: 34325274
[TBL] [Abstract][Full Text] [Related]
12. Surgical Methods and Social Factors Are Associated With Long-Term Survival in Follicular Thyroid Carcinoma: Construction and Validation of a Prognostic Model Based on Machine Learning Algorithms.
Mao Y; Huang Y; Xu L; Liang J; Lin W; Huang H; Li L; Wen J; Chen G
Front Oncol; 2022; 12():816427. PubMed ID: 35800057
[TBL] [Abstract][Full Text] [Related]
13. The efficacy of supervised learning and semi-supervised learning in diagnosis of impacted third molar on panoramic radiographs through artificial intelligence model.
Kim JY; Kahm SH; Yoo S; Bae SM; Kang JE; Lee SH
Dentomaxillofac Radiol; 2023 Sep; 52(6):20230030. PubMed ID: 37192043
[TBL] [Abstract][Full Text] [Related]
14. Machine Learning Hybrid Model for the Prediction of Chronic Kidney Disease.
Khalid H; Khan A; Zahid Khan M; Mehmood G; Shuaib Qureshi M
Comput Intell Neurosci; 2023; 2023():9266889. PubMed ID: 36959840
[TBL] [Abstract][Full Text] [Related]
15. Using Machine Learning Methods to Assess Lymphovascular Invasion and Survival in Breast Cancer: Performance of Combining Preoperative Clinical and MRI Characteristics.
Xu Z; Xie Y; Wu L; Chen M; Shi Z; Cui Y; Han C; Lin H; Liu Y; Li P; Chen X; Ding Y; Liu Z
J Magn Reson Imaging; 2023 Nov; 58(5):1580-1589. PubMed ID: 36797654
[TBL] [Abstract][Full Text] [Related]
16. Performance Analysis of Conventional Machine Learning Algorithms for Identification of Chronic Kidney Disease in Type 1 Diabetes Mellitus Patients.
Chowdhury NH; Reaz MBI; Haque F; Ahmad S; Ali SHM; A Bakar AA; Bhuiyan MAS
Diagnostics (Basel); 2021 Dec; 11(12):. PubMed ID: 34943504
[TBL] [Abstract][Full Text] [Related]
17. Machine learning model-based risk prediction of severe complications after off-pump coronary artery bypass grafting.
Zhang Y; Li L; Li Y; Zeng Z
Adv Clin Exp Med; 2023 Feb; 32(2):185-194. PubMed ID: 36226692
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Diagnostic Accuracy of Different Machine Learning Algorithms for Breast Cancer Risk Calculation: a Meta-Analysis.
Nindrea RD; Aryandono T; Lazuardi L; Dwiprahasto I
Asian Pac J Cancer Prev; 2018 Jul; 19(7):1747-1752. PubMed ID: 30049182
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
