| Title |
Improvement of Kidney Tumor Stage Classification Performance using Machine Learning Methods |
| Authors |
손호선(Ho Sun Shon) ; Kong Vungsovanreach(Kong Vungsovanreach) ; 윤석중(Seok Joong Yun) ; 오진우(Jin Woo Oh) ; 강태건(Tae Gun Kang) ; 김경아(Kyung Ah Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2023.72.11.1412 |
| Keywords |
Kidney tumor; Stage classification; Deep learning; Feature extraction |
| Abstract |
Utilizing gene expression data from kidney cancer patients, we have developed a machine learning-based deep learning algorithm to extract significant genes for predicting the patients' prognosis and enhance classification performance while addressing data imbalance issues. Particularly, classification based on tumor stage plays a crucial role in determining appropriate treatment approaches for kidney cancer patients and predicting post-treatment prognosis. We classified kidney cancer tumor stages into four categories and evaluated their performance. The results demonstrated that the SVM algorithm, utilizing an autoencoder for feature extraction and addressing data imbalance through the SMOTE technique, exhibited the best performance in terms of accuracy, recall, precision, F1-score, and AUC. These results can be utilized to choose the most suitable treatment strategy at the current state and for predicting the prognosis and enabling early diagnosis of kidney cancer. |