أعضاء هيئة التدريس - جامعة بنها
A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques
Applied Sciences • 2022
معلومات البحث
المؤلفون
Mona A. S. Ali, Rasha Orban, Rajalaxmi Rajammal Ramasamy, Suresh Muthusamy, Saanthoshkumar Subramani, Kavithra Sekar, Fathimathul Rajeena, Ibrahim Abd Elatif Gomaa, Laith Abulaigh and Diaa Salam Abd Elminaam
الكلمات المفتاحية
HCC; imbalance data; LASSO regression; ridge regression; random forest; recursive
feature elimination
المجلة العلمية
Applied Sciences
الناشر
Not Available
المجلد
12
العدد
13
الصفحات
6427
publication.type
International
رابط البحث
Open Link
المواد المرفقة
Not Available
الملخص
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 (kNN). 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.
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 (kNN). 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.