Abstract
Excessive fats in human body results in obesity, which is generally linked to various illness like heart diseases, diabetes, etc. Therefore, determining the quantity of body fat becomes essential to save the human health. Though numerous approaches are available in determining body fat percentage (BFP), intelligent and accurate models can be designed using artificial intelligence (AI) techniques. Conventional single stage methods utilized particular readings from the body or explanatory parameters in predicting BFP. In this view, this study develops a new Gravitational Search Optimization with Neutrosophic rule-based Body Fat Percentage Prediction model. The presented model intends to appropriately determine the level of BFP in an effective and automated way. To accomplish this, the proposed model follows a two-stage process namely prediction and parameter optimization. At the initial stage, the model derives a new neutrosophic set based rule classifier to determine the BFP. Secondly, the membership function in the rule based model is optimally chosen by the use of GSO algorithm and thereby results in enhanced predictive outcomes of the classification model. A wide ranging simulation analysis is performed and the results are inspected under several dimensions.
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