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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