Non-invasive assessment of NAFLD as systemic disease-A machine learning perspective.

ABSTRACT: BACKGROUND & AIMS:Current non-invasive scores for the assessment of severity of non-alcoholic fatty liver disease (NAFLD) and identification of patients with non-alcoholic steatohepatitis (NASH) have insufficient performance to be included in clinical routine. In the current study, we developed a novel machine learning approach to overcome the caveats of existing approaches. METHODS:Non-invasive parameters were selected by an ensemble feature selection (EFS) from a retrospectively collected training cohort of 164 obese individuals (age: 43.5±10.3y; BMI: 54.1±10.1kg/m2) to develop a model able to predict the histological assessed NAFLD activity score (NAS). The model was evaluated in an independent validation cohort (122 patients, age: 45.2±11.75y, BMI: 50.8±8.61kg/m2). RESULTS:EFS identifi

SUBMITTER: Canbay A 

PROVIDER: S-EPMC6435145 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature