113 related articles for article (PubMed ID: 37984098)
21. Ensemble of heterogeneous classifiers for diagnosis and prediction of coronary artery disease with reduced feature subset.
Velusamy D; Ramasamy K
Comput Methods Programs Biomed; 2021 Jan; 198():105770. PubMed ID: 33027698
[TBL] [Abstract][Full Text] [Related]
22. The birth prevalence, severity, and temporal trends of congenital heart disease in the middle-income country: A population-based study.
Mat Bah MN; Sapian MH; Jamil MT; Abdullah N; Alias EY; Zahari N
Congenit Heart Dis; 2018 Nov; 13(6):1012-1027. PubMed ID: 30289622
[TBL] [Abstract][Full Text] [Related]
23. 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]
24. Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage.
Tang J; Wang X; Wan H; Lin C; Shao Z; Chang Y; Wang H; Wu Y; Zhang T; Du Y
BMC Med Inform Decis Mak; 2022 Oct; 22(1):278. PubMed ID: 36284327
[TBL] [Abstract][Full Text] [Related]
25. Machine learning-enabled risk prediction of chronic obstructive pulmonary disease with unbalanced data.
Wang X; Ren H; Ren J; Song W; Qiao Y; Ren Z; Zhao Y; Linghu L; Cui Y; Zhao Z; Chen L; Qiu L
Comput Methods Programs Biomed; 2023 Mar; 230():107340. PubMed ID: 36640604
[TBL] [Abstract][Full Text] [Related]
26. A new hybrid ensemble machine-learning model for severity risk assessment and post-COVID prediction system.
Shakhovska N; Yakovyna V; Chopyak V
Math Biosci Eng; 2022 Apr; 19(6):6102-6123. PubMed ID: 35603393
[TBL] [Abstract][Full Text] [Related]
27. An intelligent warning model for early prediction of cardiac arrest in sepsis patients.
Layeghian Javan S; Sepehri MM; Layeghian Javan M; Khatibi T
Comput Methods Programs Biomed; 2019 Sep; 178():47-58. PubMed ID: 31416562
[TBL] [Abstract][Full Text] [Related]
28. Spatial distribution and machine learning prediction of sexually transmitted infections and associated factors among sexually active men and women in Ethiopia, evidence from EDHS 2016.
Kebede Kassaw AA; Melese Yilma T; Sebastian Y; Yeneneh Birhanu A; Sharew Melaku M; Surur Jemal S
BMC Infect Dis; 2023 Jan; 23(1):49. PubMed ID: 36690950
[TBL] [Abstract][Full Text] [Related]
29. Prediction of congenital heart disease for newborns: comparative analysis of Holt-Winters exponential smoothing and autoregressive integrated moving average models.
Xu W; Shao Z; Lou H; Qi J; Zhu J; Li D; Shu Q
BMC Med Res Methodol; 2022 Oct; 22(1):257. PubMed ID: 36183070
[TBL] [Abstract][Full Text] [Related]
30. Heterogeneous ensemble learning for enhanced crash forecasts - A frequentist and machine learning based stacking framework.
Ahmad N; Wali B; Khattak AJ
J Safety Res; 2023 Feb; 84():418-434. PubMed ID: 36868672
[TBL] [Abstract][Full Text] [Related]
31. KFPredict: An ensemble learning prediction framework for diabetes based on fusion of key features.
Qi H; Song X; Liu S; Zhang Y; Wong KKL
Comput Methods Programs Biomed; 2023 Apr; 231():107378. PubMed ID: 36731312
[TBL] [Abstract][Full Text] [Related]
32. Hybrid model for precise hepatitis-C classification using improved random forest and SVM method.
Lilhore UK; Manoharan P; Sandhu JK; Simaiya S; Dalal S; Baqasah AM; Alsafyani M; Alroobaea R; Keshta I; Raahemifar K
Sci Rep; 2023 Aug; 13(1):12473. PubMed ID: 37528148
[TBL] [Abstract][Full Text] [Related]
33. A Synthetic Data Generation Technique for Enhancement of Prediction Accuracy of Electric Vehicles Demand.
Chatterjee S; Byun YC
Sensors (Basel); 2023 Jan; 23(2):. PubMed ID: 36679392
[TBL] [Abstract][Full Text] [Related]
34. The prediction of cardiac abnormality and enhancement in minority class accuracy from imbalanced ECG signals using modified deep neural network models.
Rai HM; Chatterjee K; Dashkevych S
Comput Biol Med; 2022 Nov; 150():106142. PubMed ID: 36182760
[TBL] [Abstract][Full Text] [Related]
35. Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach.
Awad A; Bader-El-Den M; McNicholas J; Briggs J
Int J Med Inform; 2017 Dec; 108():185-195. PubMed ID: 29132626
[TBL] [Abstract][Full Text] [Related]
36. Prediction of atherosclerosis using machine learning based on operations research.
Chen Z; Yang M; Wen Y; Jiang S; Liu W; Huang H
Math Biosci Eng; 2022 Mar; 19(5):4892-4910. PubMed ID: 35430846
[TBL] [Abstract][Full Text] [Related]
37. Ensemble-based classification approach for PM2.5 concentration forecasting using meteorological data.
Saminathan S; Malathy C
Front Big Data; 2023; 6():1175259. PubMed ID: 37360751
[TBL] [Abstract][Full Text] [Related]
38. Machine learning approach to predicting persistent opioid use following lower extremity joint arthroplasty.
Gabriel RA; Harjai B; Prasad RS; Simpson S; Chu I; Fisch KM; Said ET
Reg Anesth Pain Med; 2022 May; 47(5):313-319. PubMed ID: 35115414
[TBL] [Abstract][Full Text] [Related]
39. Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease.
Shi H; Yang D; Tang K; Hu C; Li L; Zhang L; Gong T; Cui Y
Clin Nutr; 2022 Jan; 41(1):202-210. PubMed ID: 34906845
[TBL] [Abstract][Full Text] [Related]
40. Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study.
Dogan MV; Grumbach IM; Michaelson JJ; Philibert RA
PLoS One; 2018; 13(1):e0190549. PubMed ID: 29293675
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
