Colon cancer diagnosis and staging classification based on machine learning and bioinformatics analysis

Ying Su, Xuecong Tian, Rui Gao, Wenjia Guo, Cheng Chen, Chen Chen, Dongfang Jia, Hongtao Li, Xiaoyi Lv

Abstract: Highlights•Machine learning analysis combined with bioinformatics analysis to screen for markers.•Random forests perform best in both colon cancer and colon cancer staging diagnosis.•The accuracy of colon cancer diagnosis is greater than 98%.•Lasso machine learning method screens for 2 genes associated with colon cancer prognosis.•Bioinformatic analysis identified six genes associated with colon cancer prognosis distributed in five protein groups.