Application of support vector machine in cancer diagnosis.
To investigate the clinical application of tumor marker detection combined with support vector machine (SVM) model in the diagnosis of cancer. Tumor marker detection results for colorectal cancer, gastric cancer and lung cancer were collected. With these tumor mark data sets, the SVM models for diagnosis with best kernel function were created, trained and validated by cross-validation. Grid search and cross-validation methods were used to optimize the parameters of SVM. Diagnostic classifiers such as combined diagnosis test, logistic regression and decision tree were validated. Sensitivity, specialty, Youden Index and accuracy were used to evaluate the classifiers. Leave-one-out was used as the algorithm test method. For colorectal cancer, the accuracy of 4 classifiers were 75.8, 76.6, 83.1, 96.0%, respectively; for gastric cancer, the accuracy of 4 classifiers were 45.7, 64.5, 63.7, 91.7%; for lung cancer, the results were 71.9, 68.6, 75.2, 97.5%. The accuracy of SVM classifier is especially high in 4 kinds of classifiers, which indicates the potential application of SVM diagnostic model with tumor marker in cancer detection.
Wang
Hui
H
Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 1630 Dongfang Road, Pudong District, Shanghai 200127, China.
Huang
Gang
G
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000097:blood*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009369:Neoplasms / Q000097:blood* / Q000175:diagnosis*; D060388:Support Vector Machine; D055815:Young Adult
D014408:Biomarkers, Tumor