Background and Objective: Non-alcoholic fatty liver disease (NAFLD) is reported to be the only hepatic ailment increasing in its prevalence concurrently with both; obesity & Type 2 Diabetes Mellitus. Abdominal ultrasonography is done for NAFLD screening diagnosis which has a high monetary cost associated with it. • In the wake of a massive strain on global health resources due to COVID-19 pandemic, NAFLD is bound to be neglected and shelved. Machine learning is explored, here, to propose screening-diagnostic tools for NAFLD that can be easily deployed without the requirement of substantial resources and can provide instantaneous screening-diagnosis predictive results.
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