338 related articles for article (PubMed ID: 35390751)
1. The severity prediction of the binary and multi-class cardiovascular disease - A machine learning-based fusion approach.
Kibria HB; Matin A
Comput Biol Chem; 2022 Jun; 98():107672. PubMed ID: 35390751
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.
Shirwaikar RD; Acharya U D; Makkithaya K; M S; Srivastava S; Lewis U LES
Artif Intell Med; 2019 Jul; 98():59-76. PubMed ID: 31521253
[TBL] [Abstract][Full Text] [Related]
4. Score and Correlation Coefficient-Based Feature Selection for Predicting Heart Failure Diagnosis by Using Machine Learning Algorithms.
Senan EM; Abunadi I; Jadhav ME; Fati SM
Comput Math Methods Med; 2021; 2021():8500314. PubMed ID: 34966445
[TBL] [Abstract][Full Text] [Related]
5. Architectures and accuracy of artificial neural network for disease classification from omics data.
Yu H; Samuels DC; Zhao YY; Guo Y
BMC Genomics; 2019 Mar; 20(1):167. PubMed ID: 30832569
[TBL] [Abstract][Full Text] [Related]
6. Building a Cardiovascular Disease Prediction Model for Smartwatch Users Using Machine Learning: Based on the Korea National Health and Nutrition Examination Survey.
Kim MJ
Biosensors (Basel); 2021 Jul; 11(7):. PubMed ID: 34356699
[TBL] [Abstract][Full Text] [Related]
7. Survival prediction among heart patients using machine learning techniques.
Almazroi AA
Math Biosci Eng; 2022 Jan; 19(1):134-145. PubMed ID: 34902984
[TBL] [Abstract][Full Text] [Related]
8. Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method.
Limbu S; Dakshanamurthy S
Sensors (Basel); 2022 Oct; 22(21):. PubMed ID: 36365881
[TBL] [Abstract][Full Text] [Related]
9. Machine learning in the classification of asian rust severity in soybean using hyperspectral sensor.
Santana DC; Otone JDQ; Baio FHR; Teodoro LPR; Alves MEM; Junior CADS; Teodoro PE
Spectrochim Acta A Mol Biomol Spectrosc; 2024 May; 313():124113. PubMed ID: 38447444
[TBL] [Abstract][Full Text] [Related]
10. Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure.
Qiu H; Luo L; Su Z; Zhou L; Wang L; Chen Y
BMC Med Inform Decis Mak; 2020 May; 20(1):83. PubMed ID: 32357880
[TBL] [Abstract][Full Text] [Related]
11. Compare the performance of multiple binary classification models in microbial high-throughput sequencing datasets.
Xu N; Zhang Z; Shen Y; Zhang Q; Liu Z; Yu Y; Wang Y; Lei C; Ke M; Qiu D; Lu T; Chen Y; Xiong J; Qian H
Sci Total Environ; 2022 Sep; 837():155807. PubMed ID: 35537509
[TBL] [Abstract][Full Text] [Related]
12. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning.
Dinh A; Miertschin S; Young A; Mohanty SD
BMC Med Inform Decis Mak; 2019 Nov; 19(1):211. PubMed ID: 31694707
[TBL] [Abstract][Full Text] [Related]
13. Evaluation of Machine Learning Techniques for Traffic Flow-Based Intrusion Detection.
Rodríguez M; Alesanco Á; Mehavilla L; García J
Sensors (Basel); 2022 Nov; 22(23):. PubMed ID: 36502028
[TBL] [Abstract][Full Text] [Related]
14. Machine Learning Based Identification of Microseismic Signals Using Characteristic Parameters.
Peng K; Tang Z; Dong L; Sun D
Sensors (Basel); 2021 Oct; 21(21):. PubMed ID: 34770274
[TBL] [Abstract][Full Text] [Related]
15. Prediction of coronary heart disease in gout patients using machine learning models.
Jiang L; Chen S; Wu Y; Zhou D; Duan L
Math Biosci Eng; 2023 Jan; 20(3):4574-4591. PubMed ID: 36896513
[TBL] [Abstract][Full Text] [Related]
16. Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study.
Serviá L; Montserrat N; Badia M; Llompart-Pou JA; Barea-Mendoza JA; Chico-Fernández M; Sánchez-Casado M; Jiménez JM; Mayor DM; Trujillano J
BMC Med Res Methodol; 2020 Oct; 20(1):262. PubMed ID: 33081694
[TBL] [Abstract][Full Text] [Related]
17. Empirical Method for Thyroid Disease Classification Using a Machine Learning Approach.
Alyas T; Hamid M; Alissa K; Faiz T; Tabassum N; Ahmad A
Biomed Res Int; 2022; 2022():9809932. PubMed ID: 35711517
[TBL] [Abstract][Full Text] [Related]
18. Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets.
Cha GW; Moon HJ; Kim YM; Hong WH; Hwang JH; Park WJ; Kim YC
Int J Environ Res Public Health; 2020 Sep; 17(19):. PubMed ID: 32987874
[TBL] [Abstract][Full Text] [Related]
19. A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification.
Mendez KM; Reinke SN; Broadhurst DI
Metabolomics; 2019 Nov; 15(12):150. PubMed ID: 31728648
[TBL] [Abstract][Full Text] [Related]
20. Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk.
Shah A; Ahirrao S; Pandya S; Kotecha K; Rathod S
Front Public Health; 2021; 9():762303. PubMed ID: 34746087
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]