Predictive models based on machine learning for bone metastasis in patients with diagnosed colorectal cancer - Li, Tianhao; Huang, Honghong; Zhang, Shuocun; Zhang, Yongdan; Jing, Haoren; Sun, Tianwei; Zhang, Xipeng; Lu, Liangfu; Zhang, Mingqing (Frontiers in Public Health, 2022)
Background: This study aimed to develop an artificial intelligence predictive model for predicting the probability of developing BM in CRC patients. Methods: From SEER database, 17138 CRC patients were identified between January 2015 and December 2019 without missing data. SVM and LR models were trained and tested on the dataset. Accuracy, area under the curve (AUC), and IDI were used to evaluate and compare the models. Results: For bone metastases in the entire cohort, SVM model with poly as kernel function presents the best performance, whose accuracy is 0.908, recall is 0.838, and AUC is 0.908, outperforming LR model. The top three most important factors affecting the model's prediction of BM include extraosseous metastases (EM), CEA, and size. Conclusions: Our study developed an SVM model with poly as kernel function for predicting BM in CRC patients. SVM model could improve personalized clinical decision-making, help rationalize the bone metastasis screening process, and reduce the burden on healthcare systems and patients.