Analysis on risk factors for cervical cancer using induction technique

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.