Development of novel ensemble model using stacking learning and evolutionary computation techniques for automated hepatocellular carcinoma detection

• Autorzy: Ksiazek Wojciech , Hammad Mohamed , Plawiak Pawel , Acharya U. Rajendra , Tadeusiewicz Ryszard

Identyfikatory

DOI

10.1016/j.bbe.2020.08.007

• Języki publikacji: EN

Abstrakty

EN

The most common type of liver cancer is hepatocellular carcinoma (HCC), which begins in hepatocytes. The HCC, like most types of cancer, does not show symptoms in the early stages and hence it is difficult to detect at this stage. The symptoms begin to appear in the advanced stages of the disease due to the unlimited growth of cancer cells. So, early detection can help to get timely treatment and reduce the mortality rate. In this paper, we proposes a novel machine learning model using seven classifiers such as K-nearest neighbor (KNN), random forest, Naïve Bayes, and other four classifiers combined to form stacking learning (ensemble) method with genetic optimization helping to select the features for each classifier to obtain highest HCC detection accuracy. In addition to preparing the data and make it suitable for further processing, we performed the normalization techniques. We have used KNN algorithm to fill in the missing values. We trained and evaluated our developed algorithm using 165 HCC patients collected from Coimbra's Hospital and University Centre (CHUC) using stratified cross-validation techniques. There are total of 49 clinically significant features in this dataset, which are divided into two groups such as quantitative and qualitative groups. Our proposed algorithm has achieved the highest accuracy and F1-score of 0.9030 and 0.8857, respectively. The developed model is ready to be tested with huge database and can be employed in cancer screening laboratories to aid the clinicians to make an accurate diagnosis.

Słowa kluczowe

EN

HCC; stacking learning; ensemble method; machine learning; genetic algorithm

PL

HCC; uczenie zespołowe; uczenie maszynowe; algorytm genetyczny

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 4
• Strony: 1512--1524
• Opis fizyczny: Bibliogr. 59 poz., rys., tab., wykr.

Twórcy

autor

Ksiazek Wojciech

• Department of Information and Communications Technology, Faculty of Computer Science and Telecommunications, Cracow University of Technology, ul. Warszawska 24, F-3, 31-155 Krakow, Poland

autor

Hammad Mohamed

• Information Technology Department, Faculty of Computers and Information, Menoufia University, Shibin el-Kom, Menoufia, Egypt

autor

Plawiak Pawel

• Department of Information and Communications Technology, Faculty of Computer Science and Telecommunications, Cracow University of Technology, ul. Warszawska 24, F-3, 31-155 Krakow, Poland; Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Baltycka 5, 44-100 Gliwice, Poland

autor

Acharya U. Rajendra

• Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore School of Social Sciences, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taiwan

autor

Tadeusiewicz Ryszard

• Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland

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