Leveraging AutoML to provide NAFLD screening diagnosis: Proposed machine learning models
Haider Bangash, A.
Show abstract
NAFLD is reported to be the only hepatic ailment increasing in its prevalence concurrently with both; obesity & T2DM. In the wake of a massive strain on global health resources due to COVID 19 pandemic, NAFLD is bound to be neglected & shelved. Abdominal ultrasonography is done for NAFLD screening diagnosis which has a high monetary cost associated with it. We utilized MLjar, an autoML web platform, to propose machine learning models that require no coding whatsoever & take in only easy-to-measure anthropometric measures for coming up with a screening diagnosis for NAFLD with considerably high AUC. Further studies are suggested to validate the generalization of the presented models.
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