Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Maham Faheem"'
Publikováno v:
BMC Medical Research Methodology, Vol 21, Iss 1, Pp 1-7 (2021)
Abstract Background Kruskal-Wallis H test from the bank of classical statistics tests is a well-known nonparametric alternative to a one-way analysis of variance. The test is extensively used in decision-making problems where one has to compare the e
Externí odkaz:
https://doaj.org/article/655e086655af4c4aa7c4d0ca9500e32a
Publikováno v:
Pakistan Journal of Medical and Health Sciences. 16:77-80
Aim: To foresee the outcome of heart failure(HF) in Pakistani patients with potential predictors and through various machine learning (ML) methods. Study design:The secondary data of Pakistani patients is taken from the UCI repository in which a cros
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9c2c3c38ba1954eaf0829ecb1c334100
https://doi.org/10.53350/pjmhs2022161177
https://doi.org/10.53350/pjmhs2022161177
The implementation of state-of-the-art machine learning (ML) procedures for handling high dimensionality is prolonged in health care, particularly in genetics. Microarray datasets occupying a significant place in genetics are facing the problem of hi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a8adf9aaa8e96d0cbad4e347c9f7890
https://doi.org/10.21203/rs.3.rs-1931339/v1
https://doi.org/10.21203/rs.3.rs-1931339/v1
Publikováno v:
BMC Medical Research Methodology
BMC Medical Research Methodology, Vol 21, Iss 1, Pp 1-7 (2021)
BMC Medical Research Methodology, Vol 21, Iss 1, Pp 1-7 (2021)
Background Kruskal-Wallis H test from the bank of classical statistics tests is a well-known nonparametric alternative to a one-way analysis of variance. The test is extensively used in decision-making problems where one has to compare the equality o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c80244ec8875432520f3730376eee6ef
https://pubmed.ncbi.nlm.nih.gov/34657587
https://pubmed.ncbi.nlm.nih.gov/34657587