Details

Autor(en) / Beteiligte

• Lithy, Rania M.
• Omar Abdelaziz, Ashraf
• Awad, Abobakr
• Ibrahim Shousha, Hend
• Omran, Dalia
• Mahmoud Nabil, Mohamed
• Hosni Abdelmaksoud, Ahmed
• Mahmoud Elbaz, Tamer
• Mabrouk, Mahasen

Titel

Meta-learning algorithm development to predict outcomes in patients with hepatitis C virus-related hepatocellular carcinoma

Ist Teil von

• Arab journal of gastroenterology, 2022-11, Vol.23 (4), p.230-234

Ort / Verlag

Egypt: Elsevier B.V

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• Prediction of prognosis and treatment outcomes for patients with hepatocellular carcinoma (HCC) is complex for most patients. Machine learning predictive analysis can be used to explore the rich information in electronic health records to discover hidden patterns and relationships. We aimed to develop a noninvasive algorithm for predicting outcome treatment options for patients with HCC. This cross-sectional study included 1298 patients with Hepatitis C virus-related HCC attending an HCC multidisciplinary clinic, Kasr Al-Aini Hospital, Cairo University, between 2009 and 2016. Using machine learning analysis, we constructed Reduced Error Pruning (REP) decision tree algorithms and applied Auto-WEKA to select the best classifier out of 39 algorithms. The REP-tree algorithm predicted HCC management outcomes with a recall (sensitivity) of 0.658 and a precision (specificity) of 0.653 using only routine data. 854 (65.8%) instances were correctly identified, and 444 (34.2%) instances were incorrectly classified. Out of 31 attributes, liver decompensation was selected by REP-tree as the best predictor of HCC outcome (root node). With Auto-WEKA, the random subspace classifier was chosen as the best predictive algorithm with a recall (sensitivity) of 0.750 and a precision (specificity) of 0.75. There were 974 (75%) correctly classified instances and 324 (25%) incorrectly classified instances, which was better than REP-tree. Machine learning analysis explores data to discover hidden patterns and trends and enables the development of models to predict HCC treatment outcomes utilizing simple laboratory data. The random subspace classifier predicted the outcome more accurately than REP-tree.