Machine Learning Prediction Models for Diagnosing Hepatocellular Carcinoma with HCV-related Chronic Liver Disease. (November 2020)

• Record Type: Journal Article
• Authors: Hashem, Somaya; ElHefnawi, Mahmoud; Habashy, Shahira; El-Adawy, Mohamed; Esmat, Gamal; Elakel, Wafaa; Abdelazziz, Ashraf Omar; Nabeel, Mohamed Mahmoud; Abdelmaksoud, Ahmed Hosni; Elbaz, Tamer Mahmoud; Shousha, Hend Ibrahim
• Is Part Of: Computer methods and programs in biomedicine. Volume 196(2020)
• Publication Date: 2020-11
• Subjects: Hepatocellular Carcinoma prediction -- Machine Learning Algorithm -- Decision Learning Tree -- Serum marker -- Hepatitis C virus; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105551
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Machine-learning techniques can estimate HCC development risk with very high accuracy. Independent factors building the models: age, AFP, ALP, albumin, and total bilirubin. Multi-linear regression, AD Tree, REP-Tree, the CART algorithm were compared. Alternating Decision tree showed the best accuracy: 95.6% and AUC: 99.0%. Abstract: Background and Objective: Considered as one of the most recurrent types of liver malignancy, Hepatocellular Carcinoma (HCC) needs to be assessed in a non-invasive way. The objective of the current study is to develop prediction models for Chronic Hepatitis C (CHC)-related HCC using machine learning techniques. Methods: A dataset, for 4423 CHC patients, was investigated to identify the significant parameters for predicting HCC presence. In this study, several machine learning techniques (Classification and regression tree, alternating decision tree, reduce pruning error tree and linear regression algorithm) were used to build HCC classification models for prediction of HCC presence. Results: Age, alpha-fetoprotein (AFP), alkaline phosphate (ALP), albumin, and total bilirubin attributes were statistically found to be associated with HCC presence. Several HCC classification models were constructed using several machine learning algorithms. The proposed HCC classification models provide adequate area under the receiver operating characteristic curve (AUROC) and high accuracy of HCC diagnosis. AUROC ranges between 95.5% and 99%, plus overall accuracy between 93.2% and 95.6%. Conclusion: Models with simplistic factors have the power to predict the existence of HCC with outstanding performance.