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KMID : 1012020220110030285
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Physical Therapy Rehabilitation Science
2022 Volume.11 No. 3 p.285 ~ p.295
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A Comparative Study of Predictive Factors for Passing the National Physical Therapy Examination using Logistic Regression Analysis and Decision Tree Analysis
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Kim So-Hyun
Cho Sung-Hyoun
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Abstract
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Objective: The purpose of this study is to use logistic regression and decision tree analysis to identify the factors that affect the success or failurein the national physical therapy examination; and to build and compare predictive models.
Design: Secondary data analysis study
Methods: We analyzed 76,727 subjects from the physical therapy national examination data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was pass or fail, and the input variables were gender, age, graduation status, and examination area. Frequency analysis, chi-square test, binary logistic regression, and decision tree analysis were performed on the data.
Results: In the logistic regression analysis, subjects in their 20s (Odds ratio, OR＝1, reference), expected to graduate (OR＝13.616, p<0.001) and from the examination area of Jeju-do (OR＝3.135, p<0.001), had a high probability of passing. In the decision tree, the predictive factors for passing result had the greatest influence in the order of graduation status (x2＝12366.843, p<0.001) and examination area (x2＝312.446, p<0.001). Logistic regression analysis showed a specificity of 39.6% and sensitivity of 95.5%; while decision tree analysis showed a specificity of 45.8% and sensitivity of 94.7%. In classification accuracy, logistic regression and decision tree analysis showed 87.6% and 88.0% prediction, respectively.
Conclusions: Both logistic regression and decision tree analysis were adequate to explain the predictive model. Additionally, whether actual test takers passed the national physical therapy examination could be determined, by applying the constructed prediction model and prediction rate.
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KEYWORD
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Data mining, Decision tree, Logistic regression analysis, Machine learning, Physical therapy national examination
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