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Research Article | Open Access

SVM Based Classification and Prediction System for Gastric Cancer Using Dominant Features of Saliva

Muhammad Aqeel Aslam1, Cuili Xue1, Kan Wang1, Yunsheng Chen1, Amin Zhang1, Weidong Cai2, Lijun Ma3, Yuming Yang1, Xiyang Sun3, Manhua Liu1, Yunxiang Pan1, Muhammad Asif Munir4, Jie Song1, Daxiang Cui1,3,5( )
Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Centre for Intelligent Diagnosis and Treatment Instrument, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Yantai Information Technology Research Institute, Shanghai Jiao Tong University, Shanghai 200240, China
School of Computer Science, Faculty of Engineering and IT, The University of Sydney, NSW 2006, Australia
Department of Oncology, Shanghai Tongren Hospital, School of Medicine Shanghai Jiaotong University, Shanghai 200336, China
Electrical Engineering Department, Swedish College of Engineering & Technology, Rahim Yar Khan, Punjab, Pakistan
National Center for Translational Medicine, Collaborative Innovational Center for System Biology, Shanghai Jiao Tong University, Shanghai 200240, China
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Abstract

Machine learning techniques are widely used for the diagnosis of cancers. In this study, we proposed a classification and prediction system for the diagnosis of gastric cancer based on saliva samples. Gastric cancer (GC) is classified into early gastric cancer (EGC) and advanced gastric cancer (AGC). The diagnosis of GC at an early stage will improve the survival rate. Computer-aided diagnostic (CAD) systems can assist the radiologists in the diagnosis of EGC. 220 saliva samples were collected from the non-cancerous and gastric cancerous persons and analyzed using high-performance liquid chromatography-mass spectrometry (HPLC-MS). Fourteen amino acid biomarkers were sufficient to distinguish the persons from malignant to benign and were observed in the saliva samples with dominant peaks. We used the support vector machine (SVM) for binary classification. The processed Raman dataset was used to train and test the developed model. SVM based neural networks were established using different kernels, which produced different results. Accuracy, specificity, sensitivity, and receiver operating characteristics (ROC) were used to evaluate the proposed classification model, along with mean average error (MAE), mean square Error (MSE), sum average error (SAE), and sum square error (SSE). We achieved an overall accuracy of 97.18%, specificity of 97.44%, and sensitivity of 96.88% for the proposed method. This established method owns the prospect of clinical translation.

Keywords

Machine learningGastric cancer classificationSupport vector machineAccuracyDominant peaksSpecificitySensitivityROC

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Nano Biomedicine and Engineering
Volume 12 Issue 1,
March 2020
Pages 1-13
DOI: 10.5101/nbe.v12i1.p1-13
Cite this article:
Aslam MA, Xue C, Wang K, et al. SVM Based Classification and Prediction System for Gastric Cancer Using Dominant Features of Saliva. Nano Biomedicine and Engineering, 2020, 12(1): 1-13. https://doi.org/10.5101/nbe.v12i1.p1-13

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Received: 25 July 2019
Accepted: 26 December 2019
Published: 26 December 2019
© Muhammad Aqeel Aslam, Cuili Xue, Kan Wang, Yunsheng Chen, Amin Zhang, Weidong Cai, Lijun Ma, Yuming Yang, Xiyang Sun, Manhua Liu, Yunxiang Pan, Muhammad Asif Munir, Jie Song, and Daxiang Cui.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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