Prediction of Colorectal Cancer Tumor Location Using Data Mining

Abstract

Background:

Colorectal cancer is one of the most common cancers in terms of morbidity and mortality worldwide. a lot of research have been done in this field in Iran and worldwide, which have positive results. The aims of this study were firstly doing a statistical study on colorectal cancer in Mashhad, Iran, and finally predicting the colorectal location of cancer based on the clinical data by using data mining science and­ decision tree model.

Materials and Methods:

The data of 316 patients with colorectal cancer (including 14 features) were extracted from the archive of Imam Reza Hospital, Mashhad. The instrument used in this research was RapidMiner data mining software that would  try to be extract the details of the relevant data by statistical surveys and then would do initial simulations and the use of classification and decision tree method have predicteion the location of cancer.

Results:

Male to female ratio of 56% to 44%, family history of 37%, more young patients, and relatively more distally located cancers (39%) compared with the proximal (35%), and rectum (26%) were the striking findings of this study. The final and most important stage of research models were presented, which was able to predict the location of the cancerous tumor with 80% accuracy.

Conclusion:

Similarities with global statistics, such as the ratio of men to women and family history were observed. But there were also differences with global statistics including the Iran’s younger patients and relatively more patients with distal cancers. The efficiency of data mining techniques to predict the location of cancer as well as cost reduction was among the most important results of this study.