Predicting breast tumor via mining DNA viruses with decision tree

Abstract

Breast cancer is a serious problem, especially the young women in Taiwan. Until now, in the most medical researches, the reasons for suffering from breast tumor are unclear. However, most medical researches proved that DNA viruses are the high-risk factors closely related to human cancers. In recent years, hospitals and health organizations have been furnished with modern computerized medical equipment for data collection, monitoring and diagnosis. Additionally, these data are stored in large medical information systems for analysis purpose. Developing truthful and reliable classifiers for diagnosis and prognosis has become an essential task in medical and healthcare. It was reported with increasing confirmation that the machine learning algorithms can generate more accurate and transparent classifiers and decision rules for physicians than traditional methodologies. In the machine learning algorithms, decision trees have been already successfully used in the areas of medicine and healthcare. In this paper, an algorithm of decision trees, Chi-squared Automatic Interaction Detection (CHAID), is applied to build a classifier for predicting breast cancer and fibroadenoma. The results demonstrate that the decision tree technique is more favorably than logistic regression in terms of rule accuracy and knowledge transparency to physicians. Furthermore, the medical classifier can assist inexperienced physicians to prevent from misdiagnosis.