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Identification Method of Influencing Factors of Hospital Catering Service Satisfaction Based on Decision Tree Algorithm.
Li, Siyao; Xu, Di; Liu, Yang; Wang, Rui; Zhang, Jian.
  • Li S; The First Affiliated Hospital of Qiqihar Medical University, Qiqihar Medical University, Qiqihar, 161000 Heilongjiang, China.
  • Xu D; The Second Affiliated Hospital of Qiqihar Medical University, Qiqihar Medical University, Qiqihar, 161000 Heilongjiang, China.
  • Liu Y; Office of Academic Research, Qiqihar Medical University, Qiqihar, 161000 Heilongjiang, China.
  • Wang R; The First Affiliated Hospital of Qiqihar Medical University, Qiqihar Medical University, Qiqihar, 161000 Heilongjiang, China.
  • Zhang J; The First Affiliated Hospital of Qiqihar Medical University, Qiqihar Medical University, Qiqihar, 161000 Heilongjiang, China.
Appl Bionics Biomech ; 2022: 6293908, 2022.
Article in English | MEDLINE | ID: covidwho-1832694
ABSTRACT
Entering the 21st century, material abundance has been greatly enriched, and living standards have been continuously improved. Now society is gradually moving towards the era of experience economy. From the perspective of experience economy, patients' demands for hospitals are not only the satisfaction of medical technology, but their catering consumption also has begun to change to the pursuit of higher requirements. Decision tree algorithm is a kind of data mining algorithm. Data mining technology is a young technology for data analysis. It can simulate mathematical models or algorithms through data analysis, which greatly improves the prediction accuracy. This paper aims to study how to identify the influencing factors of hospital catering service satisfaction, and proposes the application of decision tree algorithm to the hospital catering service satisfaction research, and proposes decision tree-related algorithms, such as ID3, C4.5, and C5.0. Based on the analysis of patients' satisfaction with the hospital catering service in a certain hospital, the results of the model study based on the decision tree algorithm show that the risk estimation value of the training set is 0.064, and the total correct percentage is 93.6%. The risk estimate for the test set was 0.065, for a total correct percentage of 93.5%. It can be seen that the effect of the model is good and can be effectively predicted.

Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Reviews Language: English Journal: Appl Bionics Biomech Year: 2022 Document Type: Article Affiliation country: 2022

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Reviews Language: English Journal: Appl Bionics Biomech Year: 2022 Document Type: Article Affiliation country: 2022