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Title |
Comparison Multinomial Logistic Regression and Discriminant Analysis in predicting the stage of Breast Cancer |
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Author |
Doungporn Maiprasert, Krieng Kitbumrungrat |
| Citation |
Vol. 12 No. 4 pp. 44-52 |
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Abstract |
This research is a prediction stage of breast cancer group model, the probability that a patient is detected at any stage of breast cancer or non-breast cancer based on the tumor cells with abnormal growth of breast cancer. The independent variable is the tumor cells to grow abnormally: Clump Thickness (X1), Uniformity of Cell Size (X2), Uniformity of Cell Shape (X3), Marginal Adhesion (X4), Single Epithelial Cell Size (X5), Bare Nuclei (X6), Bland Chromatin (X7), Normal Nucleoli (X8), and Mitoses (X9). The dependent variable is the probability that the patient is detected at any stage of breast cancer or non-breast cancer based on the tumor cells with abnormal growth of breast cancer by using Ordinal Logistic Regression Model and Discriminant Model. Conclude that Ordinal Logistic Regression Model can use few variables in a prediction stage of breast cancer and Ordinal Logistic Regression Model has classification 55.50% higher than Discriminant Model has classification 54.10%. Ordinal Logistic Regression Model has classification for non-breast cancer patient is 73.60%, breast cancer stage 1 patient is 5%, breast cancer stage 2 patient is 43.6%, breast cancer stage 3 patient is 61.4%. The study results reveal that the Discriminant Analysis can use predicted variables 9 variables. Discriminant Model has classification for non-breast cancer patient is 56%, breast cancer stage 1 patient is 49.10%, breast cancer stage 2 patient is 35.6%, breast cancer stage 3 patient is 72.6% and breast cancer stage 4 patient is 60%. |
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Keywords |
Multinomial Logistic Regression, logistic regression, breast cancer, prediction, Discriminant Analysis |
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