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Article

A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques

by
Mona A. S. Ali
1,2,*,
Rasha Orban
2,
Rajalaxmi Rajammal Ramasamy
3,
Suresh Muthusamy
4,
Saanthoshkumar Subramani
3,
Kavithra Sekar
3,
Fathimathul Rajeena P. P.
1,*,
Ibrahim Abd Elatif Gomaa
5,
Laith Abulaigh
6 and
Diaa Salam Abd Elminaam
7,8,*
1
Computer Science Department, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 400, Saudi Arabia
2
Computer Science Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 12311, Egypt
3
Department of Computer Science and Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode 638060, India
4
Department of Electronics and Communication Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode 638060, India
5
Computer Science Department, Obour High Institute for Management and Informatics, Cairo 11777, Egypt
6
Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
7
Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 12311, Egypt
8
Computer Science Department, Faculty of Computer Science, Misr International University, Cairo 11828, Egypt
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2022, 12(13), 6427; https://doi.org/10.3390/app12136427
Submission received: 18 May 2022 / Revised: 17 June 2022 / Accepted: 19 June 2022 / Published: 24 June 2022
(This article belongs to the Special Issue Evolutionary Algorithms and Large-Scale Real-World Applications)
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Abstract

The World Health Organization (WHO) predicted that 10 million people would have died of cancer by 2020. According to recent studies, liver cancer is the most prevalent cancer worldwide. Hepatocellular carcinoma (HCC) is the leading cause of early-stage liver cancer. However, HCC occurs most frequently in patients with chronic liver conditions (such as cirrhosis). Therefore, it is important to predict liver cancer more explicitly by using machine learning. This study examines the survival prediction of a dataset of HCC based on three strategies. Originally, missing values are estimated using mean, mode, and k-Nearest Neighbor (k-NN). We then compare the different select features using the wrapper and embedded methods. The embedded method employs Least Absolute Shrinkage and Selection Operator (LASSO) and ridge regression in conjunction with Logistic Regression (LR). In the wrapper method, gradient boosting and random forests eliminate features recursively. Classification algorithms for predicting results include k-NN, Random Forest (RF), and Logistic Regression. The experimental results indicate that Recursive Feature Elimination with Gradient Boosting (RFE-GB) produces better results, with a 96.66% accuracy rate and a 95.66% F1-score.
Keywords: HCC; imbalance data; LASSO regression; ridge regression; random forest; recursive feature elimination HCC; imbalance data; LASSO regression; ridge regression; random forest; recursive feature elimination

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MDPI and ACS Style

Ali, M.A.S.; Orban, R.; Rajammal Ramasamy, R.; Muthusamy, S.; Subramani, S.; Sekar, K.; Rajeena P. P., F.; Gomaa, I.A.E.; Abulaigh, L.; Elminaam, D.S.A. A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques. Appl. Sci. 2022, 12, 6427. https://doi.org/10.3390/app12136427

AMA Style

Ali MAS, Orban R, Rajammal Ramasamy R, Muthusamy S, Subramani S, Sekar K, Rajeena P. P. F, Gomaa IAE, Abulaigh L, Elminaam DSA. A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques. Applied Sciences. 2022; 12(13):6427. https://doi.org/10.3390/app12136427

Chicago/Turabian Style

Ali, Mona A. S., Rasha Orban, Rajalaxmi Rajammal Ramasamy, Suresh Muthusamy, Saanthoshkumar Subramani, Kavithra Sekar, Fathimathul Rajeena P. P., Ibrahim Abd Elatif Gomaa, Laith Abulaigh, and Diaa Salam Abd Elminaam. 2022. "A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques" Applied Sciences 12, no. 13: 6427. https://doi.org/10.3390/app12136427

APA Style

Ali, M. A. S., Orban, R., Rajammal Ramasamy, R., Muthusamy, S., Subramani, S., Sekar, K., Rajeena P. P., F., Gomaa, I. A. E., Abulaigh, L., & Elminaam, D. S. A. (2022). A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques. Applied Sciences, 12(13), 6427. https://doi.org/10.3390/app12136427

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