Prediction of risk factors for synchronous colorectal cancer in patients with colorectal cancer

Screening for cancer and advanced treatments have not only improved treatment outcomes and patient survival rates but also led to an increase in the number of diagnosed synchronous colorectal cancer (SCC) cases. This study used machine learning techniques to develop a predictive model including seven classification techniques (naive Bayes, logistic regression, K-Star, random committee, randomizable filtered classifier, random forests, and random tree) to identify the risk factors and clinical features of SCC. The clinical dataset comprised a total of 4,287 valid records and was obtained from three cancer registries. Fourteen independent variables were selected as risk factors to analyze the characteristics of SCC. Seven classification techniques were tested in this study by using Waikato software. Performance indicators were analyzed in terms of sensitivity, accuracy, specificity, F-measure score, and precision. The results of this study revealed that the most important risk factors of SCC are combined stage, tumor size, chemotherapy, and grade/differentiation. Among the classification techniques tested, the naive Bayes method revealed the highest accuracy (90.03%). Finally, chemotherapy for SCC is an important factor for future observation.