Zobrazeno 1 - 10
of 1 915
pro vyhledávání: '"Risk models"'
Autor:
Trinh, Lua Thi
Publikováno v:
Journal of Economics, Finance and Administrative Science, 2024, Vol. 29, Issue 58, pp. 346-365.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JEFAS-04-2021-0026
Autor:
Xiaokai Yan, Yao Qi, Xinyue Yao, Lulu Yin, Hao Wang, Ji Fu, Guo Wan, Yanqun Gao, Nanjing Zhou, Xinxin Ye, Xiao Liu, Xing Chen
Publikováno v:
Biology Direct, Vol 19, Iss 1, Pp 1-18 (2024)
Abstract Background Accurately identifying effective biomarkers and translating them into clinical practice have significant implications for improving clinical outcomes in hepatocellular carcinoma (HCC). In this study, our objective is to explore ap
Externí odkaz:
https://doaj.org/article/7a4412f01cbd4f63ad65fc124dd4b681
Autor:
Lua Thi Trinh
Publikováno v:
Journal of Economics Finance and Administrative Science, Vol 29, Iss 58, Pp 346-365 (2024)
Purpose – The purpose of this paper is to compare nine different models to evaluate consumer credit risk, which are the following: Logistic Regression (LR), Naive Bayes (NB), Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Support Ve
Externí odkaz:
https://doaj.org/article/0486bddaf6934f2e8b705b4270db6b78
Autor:
XU Yunjia, SHU Biyun, ZHENG Yongtao, CHEN Ting, LAI Fenhua, NI Mengjiao, LUO Xiulan, WU Hengjing
Publikováno v:
Zhongguo quanke yixue, Vol 27, Iss 18, Pp 2192-2197 (2024)
Background With the aging trend intensifying in China, the number of super-aged population (≥80 years old) is also increasing. This demographic faces a notable decline in balance and reaction capabilities, resulting in an elevated risk of falls tha
Externí odkaz:
https://doaj.org/article/9b81ed47084f4b188d5df20646e11b78
Publikováno v:
IEEE Access, Vol 12, Pp 101497-101505 (2024)
Because cardiovascular disease (CVD) is still one of the world’s leading causes of death, sophisticated predictive models are required for early detection and prevention. This study examined how to make and compare different CVD prediction models u
Externí odkaz:
https://doaj.org/article/db73551ea649422db99785fefd04e899
Publikováno v:
Risk Management Magazine, Vol 18, Iss 3, Pp 4-15 (2023)
During the last decade, the increase in computational capacity, the consolidation of new data processing methodologies and the availability of access to new information concerning both individuals and organizations, aided by the widespread internet u
Externí odkaz:
https://doaj.org/article/8701a13b87194512a4ac90af97022acc
Autor:
Pau Codina, David Dobarro, Javier deJuan‐Bagudá, Fernando De Frutos, Josep Lupón, Antoni Bayes‐Genis, José Gonzalez‐Costello, Spanish LEVO‐D registry Collaborators
Publikováno v:
ESC Heart Failure, Vol 10, Iss 5, Pp 2875-2881 (2023)
Abstract Aims The prevalence of advanced heart failure (HF) is increasing due to the growing number of patients with HF and their better treatment and survival. There is a scarcity of data on the accuracy of HF web‐based risk scores in this selecte
Externí odkaz:
https://doaj.org/article/08179249381e4e95b71e457b0093bbe2
Publikováno v:
Mathematics, Vol 12, Iss 11, p 1763 (2024)
The quality performance of many vital systems depends on how long the units are performing; hence, research works started focusing on increasing the reliability of systems while taking into consideration that many factors may cause the failures of op
Externí odkaz:
https://doaj.org/article/450df16d30f64944b358bf4d045c8781
Autor:
Maria A. Zolotykh, Airat I. Bilyalov, Alfiya I. Nesterova, Albert M. Gimranov, Julia V. Filina, Albert A. Rizvanov, Regina R. Miftakhova
Publikováno v:
Современная онкология, Vol 25, Iss 2, Pp 190-198 (2023)
Determination of cancer risk factors allow us to develop diagnostics tests that improved identification and reduced the rate of mortality of most frequent cancer diseases including breast cancer, prostate cancer, gastrointestinal tumors. Today indivi
Externí odkaz:
https://doaj.org/article/93e1432c7a7c48c7bfccb91950bb5a53
Publikováno v:
Frontiers in Pharmacology, Vol 14 (2024)
Objective: To develop a risk score model for the occurrence of composite cardiovascular events (CVE) in patients with stable angina pectoris (SA) combined with coronary heart disease (CHD) by comparing the modeling effects of various machine learning
Externí odkaz:
https://doaj.org/article/f2bc5a3105804a7ab65ba6fbea57a5d4