A novel machine learning approach for early detection of hepatocellular carcinoma patients |
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| Institution: | 1. Department of Radiology, Guangdong Provincial People''s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China;2. School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China;3. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;4. Guangzhou Panyu Central Hospital, Guangzhou, China;1. Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan;2. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan;3. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan;4. Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan |
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| Abstract: | Liver cancer is quite common type of cancer among individuals worldwide. Hepatocellular carcinoma (HCC) is the malignancy of liver cancer. It has high impact on individual’s life and investigating it early can decline the number of annual deaths. This study proposes a new machine learning approach to detect HCC using 165 patients. Ten well-known machine learning algorithms are employed. In the preprocessing step, the normalization approach is used. The genetic algorithm coupled with stratified 5-fold cross-validation method is applied twice, first for parameter optimization and then for feature selection. In this work, support vector machine (SVM) (type C-SVC) with new 2level genetic optimizer (genetic training) and feature selection yielded the highest accuracy and F1-Score of 0.8849 and 0.8762 respectively. Our proposed model can be used to test the performance with huge database and aid the clinicians. |
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| Keywords: | Machine learning Data mining Hepatocellular carcinoma (HCC) Genetic algorithm Normalization Feature selection |
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