Development of Prediction Model for 5-year Survival of Colorectal Cancer.

OBJECTIVES

This study aims to introduce a prediction model based on a machine learning approach as an efficient solution for prediction purposes to better prognosis and increase CRC survival.

METHODS

In the current retrospective study, we used the data of 1062 CRC cases to analyse and establish a prediction model for the 5-year CRC survival. The machine learning algorithms were used to develop prediction models, including random Forest, XG-Boost, bagging, logistic regression, support vector machine, artificial neural network, decision tree, and K-nearest neighbours.

RESULTS

The current study revealed that the XG-Boost with AU-ROC of 0.906 and 0.813 for internal and external conditions gave us better insight into predictability and generalizability than other algorithms.

CONCLUSION

XG-Boost can be utilised as a knowledge source for implementing intelligent systems as an assistive tool for clinical decision-making in healthcare settings to improve prognosis and increase CRC survival through various clinical solutions that doctors can achieve.