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

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 frequent...

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Bibliographic Details
Published in:Applied sciences 2022-07, Vol.12 (13), p.6427
Main Authors: Ali, Mona A. S., Orban, Rasha, Rajammal Ramasamy, Rajalaxmi, Muthusamy, Suresh, Subramani, Saanthoshkumar, Sekar, Kavithra, Rajeena P. P., Fathimathul, Gomaa, Ibrahim Abd Elatif, Abulaigh, Laith, Elminaam, Diaa Salam Abd
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Cirrhosis
Classification
Datasets
Diabetes
Feature selection
HCC
Hepatitis
Hepatocellular carcinoma
imbalance data
LASSO regression
Liver
Liver cancer
Liver cirrhosis
Liver diseases
Machine learning
Methods
Neural networks
random forest
recursive feature elimination
Regression analysis
ridge regression
Sampling techniques
Survival
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12136427