TY - JOUR
T1 - Prediction of cervical lymph nodes recurrence after radiotherapy for early nasopharyngeal carcinoma via unsupervised diagnostic feature learning and supervised ensemble classifier learning
AU - Lu, Zhenkun
AU - Wei, Haohan
AU - Ye, Fengyu
AU - Li, Sheng
AU - Huang, Qinghua
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/5
Y1 - 2024/5
N2 - Purpose: Nasopharyngeal carcinoma is one of the most prevalent malignant tumors in Guangdong, China. In the field of medicine, predicting the recurrence of early-stage nasopharyngeal carcinoma after radiotherapy holds significant importance. Our objective was to develop a novel classification model aimed at predicting recurrence following radiotherapy for early nasopharyngeal carcinoma. Methods and Materials: This paper introduces an innovative approach by combining unsupervised diagnostic feature learning (biclustering algorithm) with Adaboost ensemble learning to create a novel classification model. Notably, the nasopharyngeal carcinoma dataset underwent biclustering algorithm application for the first time. Initially, the Borderline-SMOTE oversampling algorithm was employed to address the dataset's imbalance issue. Subsequently, the biclustering algorithm, which is based on an improved multi-objective genetic algorithm (NSGA-II), was utilized to seek bicluster outcomes exhibiting consistent representation patterns. The attributes of these bicluster outcomes were assessed and employed as diagnostic rules for constructing weak classifiers. Ultimately, the Adaboost ensemble learning technique was employed to amalgamate the weak classifiers into a robust classifier. Results: Following the application of 10-fold cross-validation, the model exhibited an accuracy of 80.33%, sensitivity of 79.55%, specificity of 80.43%, and GMean of 79.99%, accompanied by an AUC value of 0.904. Conclusions: The presented classification model outperformed alternative classification models in terms of classification accuracy and generalization when applied to the nasopharyngeal carcinoma dataset. Consequently, it serves as a valuable tool to aid medical professionals in predicting the likelihood of recurrence in the cervical lymph nodes following radiotherapy for early-stage nasopharyngeal carcinoma.
AB - Purpose: Nasopharyngeal carcinoma is one of the most prevalent malignant tumors in Guangdong, China. In the field of medicine, predicting the recurrence of early-stage nasopharyngeal carcinoma after radiotherapy holds significant importance. Our objective was to develop a novel classification model aimed at predicting recurrence following radiotherapy for early nasopharyngeal carcinoma. Methods and Materials: This paper introduces an innovative approach by combining unsupervised diagnostic feature learning (biclustering algorithm) with Adaboost ensemble learning to create a novel classification model. Notably, the nasopharyngeal carcinoma dataset underwent biclustering algorithm application for the first time. Initially, the Borderline-SMOTE oversampling algorithm was employed to address the dataset's imbalance issue. Subsequently, the biclustering algorithm, which is based on an improved multi-objective genetic algorithm (NSGA-II), was utilized to seek bicluster outcomes exhibiting consistent representation patterns. The attributes of these bicluster outcomes were assessed and employed as diagnostic rules for constructing weak classifiers. Ultimately, the Adaboost ensemble learning technique was employed to amalgamate the weak classifiers into a robust classifier. Results: Following the application of 10-fold cross-validation, the model exhibited an accuracy of 80.33%, sensitivity of 79.55%, specificity of 80.43%, and GMean of 79.99%, accompanied by an AUC value of 0.904. Conclusions: The presented classification model outperformed alternative classification models in terms of classification accuracy and generalization when applied to the nasopharyngeal carcinoma dataset. Consequently, it serves as a valuable tool to aid medical professionals in predicting the likelihood of recurrence in the cervical lymph nodes following radiotherapy for early-stage nasopharyngeal carcinoma.
KW - Diagnostic feature learning
KW - Early nasopharyngeal carcinoma
KW - Ensemble learning
KW - Prediction
KW - Recurrence
UR - http://www.scopus.com/inward/record.url?scp=85184148201&partnerID=8YFLogxK
U2 - 10.1016/j.bspc.2024.106075
DO - 10.1016/j.bspc.2024.106075
M3 - 文章
AN - SCOPUS:85184148201
SN - 1746-8094
VL - 91
JO - Biomedical Signal Processing and Control
JF - Biomedical Signal Processing and Control
M1 - 106075
ER -