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http://dx.doi.org/10.9708/jksci.2021.26.07.009

Classification models for chemotherapy recommendation using LGBM for the patients with colorectal cancer  

Oh, Seo-Hyun (Dept. of Computer Engineering, Gachon University)
Baek, Jeong-Heum (Dept. of Surgery, Gil Medical Center)
Kang, Un-Gu (Dept. of Computer Engineering, Gachon University)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.7, 2021 , pp. 9-17 More about this Journal
Abstract
In this study, we propose a part of the CDSS(Clinical Decision Support System) study, a system that can classify chemotherapy, one of the treatment methods for colorectal cancer patients. In the treatment of colorectal cancer, the selection of chemotherapy according to the patient's condition is very important because it is directly related to the patient's survival period. Therefore, in this study, chemotherapy was classified using a machine learning algorithm by creating a baseline model, a pathological model, and a combined model using both characteristics of the patient using the individual and pathological characteristics of colorectal cancer patients. As a result of comparing the prediction accuracy with Top-n Accuracy, ROC curve, and AUC, it was found that the combined model showed the best prediction accuracy, and that the LGBM algorithm had the best performance. In this study, a chemotherapy classification model suitable for the patient's condition was constructed by classifying the model by patient characteristics using a machine learning algorithm. Based on the results of this study in future studies, it will be helpful for CDSS research by creating a better performing chemotherapy classification model.
Keywords
Colorectal Cancer; Chemotherapy; Artificial Intelligence; Machine learning; classification model;
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