Predicting intraventricular hemorrhage growth with a machine learning-based, radiomics-clinical model

Dong-Qin Zhu; Qian Chen; Yi-Lan Xiang; Chen-Yi Zhan; Ming-Yue Zhang; Chao Chen; Qi-Chuan Zhuge; Wei-Jian Chen; Xiao-Ming Yang; Yun-Jun Yang

doi:doi:10.18632/aging.202954

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Research Paper Volume 13, Issue 9 pp 12833—12848

Predicting intraventricular hemorrhage growth with a machine learning-based, radiomics-clinical model

Figure 6. (A) Regions of interest were manually segmented. (B) A total of 396 features were extracted. (C) Features were selected using LASSO method. (D) Rad-score was calculated. (E) Predicting model was developed using support vector machine.