Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study Publisher Pubmed



Mohammadi G¹ ; Looha MA² ; Pourhoseingholi MA³ ; Tavirani MR⁴ ; Sohrabi S⁵ ; Khaneh AZS⁶ ; Piri H⁷ ; Alaei T⁸ ; Parvani N⁵ ; Vakilzadeh I⁵ ; Javadi S⁹ ; Cheshmeh ZMH¹⁰ ; Razzaghi Z¹¹ ; Robati RM¹² Show All Authors
Source: Asian Pacific Journal of Cancer Prevention Published:2024

Abstract

Introduction: Colorectal cancer (CRC) ranks as the second leading cause of cancer-related deaths. This study aimed to predict survival outcomes of CRC patients using machine learning (ML) methods. Material and Methods: A retrospective analysis included 1853 CRC patients admitted to three prominent tertiary hospitals in Iran from October 2006 to July 2019. Six ML methods, namely logistic regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Neural Network (NN), Decision Tree (DT), and Light Gradient Boosting Machine (LGBM), were developed with 10-fold cross-validation. Feature selection employed the Random Forest method based on mean decrease GINI criteria. Model performance was assessed using Area Under the Curve (AUC). Results: Time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type emerged as crucial predictors of survival based on mean decrease GINI. The NB (AUC = 0.70, 95% Confidence Interval [CI] 0.65–0.75) and LGBM (AUC = 0.70, 95% CI 0.65–0.75) models achieved the highest predictive AUC values for CRC patient survival. Conclusions: This study highlights the significance of variables including time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type in predicting CRC survival. The NB model exhibited optimal efficacy in mortality prediction, maintaining a balanced sensitivity and specificity. Policy recommendations encompass early diagnosis and treatment initiation for CRC patients, improved data collection through digital health records and standardized protocols, support for predictive analytics integration in clinical decisions, and the inclusion of identified prognostic variables in treatment guidelines to enhance patient outcomes. © (2024) This work is licensed under a Creative Commons Attribution-Non Commercial 4.0 International License.