Machine learning as an early warning system to predict financial crisis

2020 ◽  
Vol 71 ◽  
pp. 101507 ◽  
Author(s):  
Aristeidis Samitas ◽  
Elias Kampouris ◽  
Dimitris Kenourgios
Keyword(s):  
Machine Learning ◽  
Financial Crisis ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System

scholarly journals Clinician Perception of a Machine Learning–Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock*

2019 ◽  
Vol 47 (11) ◽  
pp. 1477-1484 ◽  
Author(s):  
Jennifer C. Ginestra ◽  
Heather M. Giannini ◽  
William D. Schweickert ◽  
Laurie Meadows ◽  
Michael J. Lynch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Septic Shock ◽  
Severe Sepsis ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Sepsis And Septic Shock

scholarly journals Establishment of a Financial Crisis Early Warning System for Domestic Listed Companies Based on Three Decision Tree Models

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Gang Wang ◽  
Keming Wang ◽  
Yingying Zhou ◽  
Xiaoyan Mo ◽  
Weilin Xiao
Keyword(s):  
Financial Crisis ◽  
Decision Tree ◽  
Early Warning ◽  
Financial Management ◽  
Early Warning System ◽  
Comprehensive Evaluation ◽  
Warning System ◽  
Predictive Ability ◽  
Tree Models ◽  
The Impact

The financial crisis is a realistic problem that the general enterprise must encounter in the process of financial management. Due to the impact of the COVID-19 and the Sino-US trade war, domestic companies with unsound financial conditions are at risk of shutdowns and bankruptcies. Therefore, it is urgently needed to study the financial warning of enterprises. In this study, three decision tree models are used to establish the financial crisis early warning system. These three decision tree models include C50, CART, and random forest decision trees. In addition, the ROC curve was used for comprehensive evaluation of the accuracy analysis of the model to confirm the predictive ability of each model. This result can provide reference for domestic financial departments and provide financial management basis for the investing public.

Reducing Intraoperative Hypotension Using a Machine Learning–Derived Early Warning System—Reply

JAMA ◽  
2020 ◽  
Vol 324 (8) ◽  
pp. 807
Author(s):  
Bart F. Geerts ◽  
Alexander P. Vlaar ◽  
Denise P. Veelo
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Intraoperative Hypotension

scholarly journals Early Warning System of Housing Market Using Machine Learning

2021 ◽  
Vol 7 (1) ◽  
pp. 29-45
Author(s):  
Daehyeon Park ◽  
Jeonghwan Kim ◽  
Doojin Ryu
Keyword(s):  
Machine Learning ◽  
Housing Market ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System

A Novel Early Warning System of Oil Production Based on Machine Learning

2019 ◽  
Author(s):  
Kang Ma ◽  
Hanqiao Jiang ◽  
Junjian Li ◽  
Rongda Zhang ◽  
Lufeng Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Oil Production
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