Social Security Organization, MSC student in Data Science
Abstract
Objective: Data mining consists of the science and techniques used to analyze data in order to discover and extract previously unknown patterns. It is also considered as a key part of the knowledge discovery process in databases. Our main goal is to build an efficient, high-precision classification model to improve efficiency and effectiveness. Method: In this paper, we introduce a supervised learning technique to create a decision tree for the survey data of the SSO Martyred-Workers Hospital in Yazd Province. The main goal is to build an efficient classification model with high accuracy to improve the efficiency and effectiveness of the admission process. To build the decision tree, we used the CART algorithm and the rpart package in the R programming language, and the final model was evaluated using common evaluation methods. Findings: The findings of this study showed special results with the help of which we were able to classify the available data, which is very important in managerial decisions. Conclusion: According to the result, the most important categories are (from the right) the first and third categories, respectively, because they comprise about 75% of our data.
Shahriari, S. (2021). A Survey on Decision Tree Algorithms of Classification in Data Mining The SSO Martyred-Workers Hospital – Yazd Province. Social Security Journal, 17(2), 111-120.
MLA
Samad Shahriari. "A Survey on Decision Tree Algorithms of Classification in Data Mining The SSO Martyred-Workers Hospital – Yazd Province". Social Security Journal, 17, 2, 2021, 111-120.
HARVARD
Shahriari, S. (2021). 'A Survey on Decision Tree Algorithms of Classification in Data Mining The SSO Martyred-Workers Hospital – Yazd Province', Social Security Journal, 17(2), pp. 111-120.
VANCOUVER
Shahriari, S. A Survey on Decision Tree Algorithms of Classification in Data Mining The SSO Martyred-Workers Hospital – Yazd Province. Social Security Journal, 2021; 17(2): 111-120.
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