title: A new cluster-based oversampling method for improving survival prediction of hepatocellular carcinoma patients.
abstract: Liver cancer is the sixth most frequently diagnosed cancer and, particularly, Hepatocellular Carcinoma (HCC) represents more than 90% of primary liver cancers. Clinicians assess each patient's treatment on the basis of evidence-based medicine, which may not always apply to a specific patient, given the biological variability among individuals. Over the years, and for the particular case of Hepatocellular Carcinoma, some research studies have been developing strategies for assisting clinicians in decision making, using computational methods (e.g. machine learning techniques) to extract knowledge from the clinical data. However, these studies have some limitations that have not yet been addressed: some do not focus entirely on Hepatocellular Carcinoma patients, others have strict application boundaries, and none considers the heterogeneity between patients nor the presence of missing data, a common drawback in healthcare contexts. In this work, a real complex Hepatocellular Carcinoma database composed of heterogeneous clinical features is studied. We propose a new cluster-based oversampling approach robust to small and imbalanced datasets, which accounts for the heterogeneity of patients with Hepatocellular Carcinoma. The preprocessing procedures of this work are based on data imputation considering appropriate distance metrics for both heterogeneous and missing data (HEOM) and clustering studies to assess the underlying patient groups in the studied dataset (K-means). The final approach is applied in order to diminish the impact of underlying patient profiles with reduced sizes on survival prediction. It is based on K-means clustering and the SMOTE algorithm to build a representative dataset and use it as training example for different machine learning procedures (logistic regression and neural networks). The results are evaluated in terms of survival prediction and compared across baseline approaches that do not consider clustering and/or oversampling using the Friedman rank test. Our proposed methodology coupled with neural networks outperformed all others, suggesting an improvement over the classical approaches currently used in Hepatocellular Carcinoma prediction models.
authors.affiliation: Centre for Informatics and Systems, University of Coimbra, Pólo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal; Department of Informatics Engineering, Faculty of Sciences and Technology, University of Coimbra, Pólo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal. Electronic address: miriams@student.dei.uc.pt.
authors.forename: Miriam Seoane
authors.initials: MS
authors.lastname: Santos
authors.affiliation: Centre for Informatics and Systems, University of Coimbra, Pólo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal; Department of Informatics Engineering, Faculty of Sciences and Technology, University of Coimbra, Pólo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal. Electronic address: pha@dei.uc.pt.
authors.forename: Pedro Henriques
authors.initials: PH
authors.lastname: Abreu
authors.affiliation: Centro Universitario de la Defensa de San Javier (University Centre of Defence at the Spanish Air Force Academy), MDE-UPCT, Calle Coronel López Peña, s/n, 30720 Santiago de la Ribera, Murcia, Spain. Electronic address: pedroj.garcia@cud.upct.es.
authors.forename: Pedro J
authors.initials: PJ
authors.lastname: García-Laencina
authors.affiliation: Internal Medicine Service, Hospital and University Centre of Coimbra, EPE, Rua Fonseca Pinto, 3000-075 Coimbra, Portugal. Electronic address: adeliasimao@gmail.com.
authors.forename: Adélia
authors.initials: A
authors.lastname: Simão
authors.affiliation: Internal Medicine Service, Hospital and University Centre of Coimbra, EPE, Rua Fonseca Pinto, 3000-075 Coimbra, Portugal. Electronic address: aspcarvalho@gmail.com.
authors.forename: Armando
authors.initials: A
authors.lastname: Carvalho
mesh_terms: D006528:Carcinoma, Hepatocellular / Q000503:physiopathology*; D016000:Cluster Analysis; D006801:Humans; D008113:Liver Neoplasms / Q000503:physiopathology*
keywords: Clustering; Hepatocellular Carcinoma (HCC); K-means; Oversampling; SMOTE; Survival prediction