Analysis on risk factors for cervical cancer using induction technique

Authors:
Seung Hee Ho;Sun Ha Jee;Jong Eun Lee;Jong Sup Park
Affiliations:
Graduate School of Management, Korea Advanced Institute of Science and Technology, 207-43 Cheongryangri-dong, Dongdaemoon-gu, Seoul 130-012, South Korea;Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, PO BOX 8044, Seoul 120-749, South Korea;DNA Link, Inc. Seoul, South Korea;Department of Obstetrics and Gynecology, College of Medicine, Catholic University, 505 Banpo-dong, Seocho-gu, Seoul, South Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2004

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Abstract

Cervical cancer is a leading cause of cancer deaths in woman worldwide. New approach to the analysis of risk factors and management of cervical cancer is discussed in this study. We identified the combined patterns of cervical cancer risk factors including demographic, environmental and genetic factors using induction technique. We compared logistic regression and a decision tree algorithm, CHAID (Chi-squared Automatic Interaction Detection), using a test set of 133 participants and a training set of 577 participants. The CHAID had a better predictive rate and sensitivity (72.96 and 64.00%, respectively) than logistic regression (71.83 and 40.80%, respectively). However, the CHAID had lower specificity (77.83%) than logistic regression (88.70%). In addition, we demonstrated how the decision tree algorithm could be used in risk analysis and target segmentation for cervical cancer management. This is the first study using induction technique for the analysis of risk factors for cervical cancer, and the results of this study will contribute to developing the clinical practice guideline for cervical cancer.