A reliable method for colorectal cancer prediction based on feature selection and support vector machine.
Colorectal cancer (CRC) is a common cancer responsible for approximately 600,000 deaths per year worldwide. Thus, it is very important to find the related factors and detect the cancer accurately. However, timely and accurate prediction of the disease is challenging. In this study, we build an integ...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 4; pp. 901 - 913 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Apr2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135753238&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135753238 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2019 vid: 57 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135753238 135753238 NLM30478811 10.1007/s11517-018-1930-0 NLM30478811 135753238 ppf: 901 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A reliable method for colorectal cancer prediction based on feature selection and support vector machine. aug: au: Zhao, Dandan Liu, Hong Zheng, Yuanjie He, Yanlin Lu, Dianjie Lyu, Chen affil: Shandong Normal University, School of Information Science and Engineering, No. 88, Wenhua East Road, Jinan, People's Republic of China sug: subj: Colorectal Neoplasms Diagnosis Body Mass Index Logistic Regression Pharmacokinetics Reproducibility of Results Sensitivity and Specificity Models, Biological ROC Curve Colorectal Neoplasms Mortality Arthritis Impact Measurement Scales ab: Colorectal cancer (CRC) is a common cancer responsible for approximately 600,000 deaths per year worldwide. Thus, it is very important to find the related factors and detect the cancer accurately. However, timely and accurate prediction of the disease is challenging. In this study, we build an integrated model based on logistic regression (LR) and support vector machine (SVM) to classify the CRC into cancer and normal samples. From various factors, human location, age, gender, BMI, and cancer tumor type, tumor grade, and DNA, of the cancer, we select the most significant factors (p < 0.05) using logistic regression as main features, and with these features, a grid-search SVM model is designed using different kernel types (Linear, radial basis function (RBF), Sigmoid, and Polynomial). The result of the logistic regression indicates that the Firmicutes (AUC 0.918), Bacteroidetes (AUC 0.856), body mass index (BMI) (AUC 0.777), and age (AUC 0.710) and their combined factors (AUC 0.942) are effective for CRC detection. And the best kernel type is RBF, which achieves an accuracy of 90.1% when k = 5, and 91.2% when k = 10. This study provides a new method for colorectal cancer prediction based on independent risky factors. Graphical abstract Flow chart depicting the method adopted in the study. LR (logistic regression) and ROC curve are used to select independent features as input of SVM. SVM kernel selection aims to find the best kernel function for classification by comparing Linear, RBF, Sigmoid, and Polynomial kernel types of SVM, and the result shows the best kernel is RBF. Classification performance of LR + RF, LR + NB, LR + KNN, and LR + ANNs models are compared with LR + SVM. After these steps, the cancer and healthy individuals can be classified, and the best model is selected. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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