Non-Alcoholic Fatty Liver Disease Diagnosis with Multi-Group Factors

Abstract

Although various clinical factors affect the diagnosis of Non-alcoholic Fatty Liver Disease (NAFLD), most studies only use single-source data such as images or laboratory data. Nevertheless, using different categories of features can help to get better results. Hence, one of the most important purposes of this paper is to employ a multi-group of effective factors such as velocimetry, psychological, demographic and anthropometric, and lab test data. Then, some Machine Learning (ML) methods are applied to classify the samples into two healthy and patient with NAFLD groups. The data used here belongs to the PERSIAN Organizational Cohort study at Mashhad University of Medical Sciences. To quantify the scalability of the models, different validity metrics are used. The obtained results illustrate that the proposed method can lead to an increase in the efficiency of the classifiers.

Keywords: Classification; Multi-Group Factors; NAFLD Diagnose.