Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence
Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence
C. Tseng,Chi-Jie Lu,2 Authors,Chalong Cheewakriangkrai
2017 · DOI: 10.1016/j.artmed.2017.06.003
71 Citations
TLDR
Empirical results illustrated that the integrated C5.0 model is a superior approach in predicting the recurrence of ovarian cancer, and suggested The International Federation of Gynecology and Obstetrics, Pathologic M, Age, and Pathologic T were the four most critical risk factors for ovarian cancer recurrence.