Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression

Shayan, Zahra and Mohammad Gholi Mezerji, Naser and Shayan, Leila and Naseri, Parisa (2015) Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression. Global Journal of Health Science, 8 (7). p. 41. ISSN 1916-9736

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Abstract

BACKGROUND: Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices.

METHODS: This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models.

RESULTS: CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model.

CONCLUSION: The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

Item Type: Article
Subjects: OA Library Press > Medical Science
Depositing User: Unnamed user with email support@oalibrarypress.com
Date Deposited: 03 May 2023 05:58
Last Modified: 04 Sep 2024 03:57
URI: http://archive.submissionwrite.com/id/eprint/801

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