Hepatic steatosis has become a serious health concern among the general population, but especially for those who are obese. Liver fat can increase the risk of cirrhosis and even liver cancer. Current standard methods to assess hepatic steatosis, such as liver biopsy and CT/MR imaging techniques, are expensive and/or may have associated risks to health. In this paper, we use body shapes to assess hepatic steatosis using both traditional linear regression models and a deep neural network. We apply our models to a medical dataset and evaluate the approaches for both regression and classification. We compare the performance of several models via popular evaluation metrics. The experimental results indicate that our proposed neural network outperforms the vanilla linear regression model by 22.37% in RMSE and the accuracy by 18%. The R-squared value of the neural model is more than 0.72 and the accuracy reaches 78%. Hence, the body shape features can provide an additional accurate and affordable choice to monitor the degree of the patient's liver fat. Clinical relevance - This paper presents a low cost and convenient approach to predict liver fat percentage using body shapes. This approach will not replace the gold standard for assessing hepatic steatosis. However, with the wide availability for depth cameras (including on smartphones), the approach promises to provide another modality that can be deployed widely in clinical setting as well for home use for telehealth.