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A novel machine learning approach for early detection of hepatocellular carcinoma patients
journal contribution
posted on 2019-05-01, 00:00 authored by W Książek, Moloud Abdar, U R Acharya, P Pławiak© 2018 Elsevier B.V. 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 F 1 -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.
History
Journal
Cognitive Systems ResearchVolume
54Pagination
116 - 127Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
eISSN
1389-0417Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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Keywords
Machine learningData miningHepatocellular carcinoma (HCC)Genetic algorithmNormalizationFeature selectionScience & TechnologySocial SciencesTechnologyLife Sciences & BiomedicineComputer Science, Artificial IntelligenceNeurosciencesPsychology, ExperimentalComputer ScienceNeurosciences & NeurologyPsychologyRANDOM FORESTECG SIGNALSCLASSIFICATIONRECOGNITIONPREDICTIONHYBRIDIDENTIFICATIONCLASSIFIERSDIAGNOSISPhilosophy
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