March 29, 2024, 4:42 a.m. | Mahbubunnabi Tamala, Mohammad Marufur Rahmanb, Maryam Alhasimc, Mobarak Al Mulhimd, Mohamed Derichee

cs.LG updates on

arXiv:2403.19355v1 Announce Type: new
Abstract: For severely affected COVID-19 patients, it is crucial to identify high-risk patients and predict survival and need for intensive care (ICU). Most of the proposed models are not well reported making them less reproducible and prone to high risk of bias particularly in presence of imbalance data/class. In this study, the performances of nine machine and deep learning algorithms in combination with two widely used feature selection methods were investigated to predict last status representing …

abstract artificial artificial intelligence arxiv bias covid covid-19 cs.lg identify intelligence making mortality patients prediction risk survival them type

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