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Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Abdullah, Ahsan
Hussain, Amir
Khan, Imtiaz Hussain
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Title: Predicting obesity using longitudinal near infra-red spectroscopy (NIRS) data
Citation: Abdullah A, Hussain A & Khan IH (2017) Predicting obesity using longitudinal near infra-red spectroscopy (NIRS) data In: ICCDA '17 Proceedings of the International Conference on Compute and Data Analysis, New York: ACM. ICCDA '17: International Conference on Compute and Data Analysis, 19.5.2017 - 23.5.2017, Lakeland, FL, USA, pp. 123-128.
Issue Date: 2017
Conference Name: ICCDA '17: International Conference on Compute and Data Analysis
Conference Dates: 2017-05-19T00:00:00Z
Conference Location: Lakeland, FL, USA
Abstract: Globally there has been a dramatic increase in obesity [1]. Thus understanding, predicting and managing obesity has the potential to save lives and billions. Behavioral studies suggest that binging by obese persons is prompted by inflated brain reward center activity to stimuli linked with high-calorie foods [2], but there are hardly any data-analytic calorie-based cognitive studies using non-invasive Near-Infrared Spectroscopy (NIRS) data that predict obesity using predictive data mining. In this paper, details of a novel research methodology are presented for a 24-month longitudinal NIRS study in natural subject environments. The proposed methodology is based on brain reward center activation mapping, simulated results of Naïve Bayes modeling using these activation maps demonstrate how cerebral functional activity data can be used to predict obesity in the non-obese.
Status: Book Chapter: publisher version
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