AUTHOR=Li Tianhao , Huang Honghong , Zhang Shuocun , Zhang Yongdan , Jing Haoren , Sun Tianwei , Zhang Xipeng , Lu Liangfu , Zhang Mingqing TITLE=Predictive models based on machine learning for bone metastasis in patients with diagnosed colorectal cancer JOURNAL=Frontiers in Public Health VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.984750 DOI=10.3389/fpubh.2022.984750 ISSN=2296-2565 ABSTRACT=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, 50,566 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 0.926, outperforming LR model. The top three most important factors affecting the model's prediction of BM include extraosseous metastases (EM), CEA, and size.

Conclusion

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.