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Abstract #3488

Predicting the efficacy of neoadjuvant chemo-radiotherapy for advanced rectal cancer using random forest model and a paired-difference up-sampling strategy under small sample size

Jie Kuang1, QingLei Shi2, Gaofeng Shi1, Xu Yan3, and LI Yang1
1The Fourth Hospital of Hebei Medical University, Shijiazhuang, China, 2Siemens Healthcare, MR Scientific Marketing, Beijing, China, 3Siemens Healthcare, MR Scientific Marketing, Shanghai, China

In this study, we adopted a paired-difference strategy, which can improve training efficiency of random forest (RF) model with a small sample size. Through optimizing in normalization, dimensional reduction, and features selection steps, a higher accuracy was achieved in predicting the efficacy of chemo-radiotherapy for advanced rectal cancer.

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