Predicting obesity using longitudinal near infra-red spectroscopy (NIRS) data

Abdullah, A.; Hussain, A.; Khan, I.H.

Abstract

Globally there has been a dramatic increase in obesity. 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, 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.

Citation

Abdullah, A., Hussain, A., & Khan, I. (2017, May). Predicting obesity using longitudinal near infra-red spectroscopy (NIRS) data. Presented at ICCDA '17: International Conference on Compute and Data Analysis, Lakeland, FL, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: ICCDA '17: International Conference on Compute and Data Analysis
• Start Date: May 19, 2017
• End Date: May 23, 2017
• Publication Date: 2017
• Deposit Date: Sep 23, 2019
• Publisher: Association for Computing Machinery (ACM)
• Pages: 123-128
• Book Title: Proceedings of the International Conference on Compute and Data Analysis
• ISBN: 978-1-4503-5241-3
• DOI: https://doi.org/10.1145/3093241.3093286
• Public URL: http://researchrepository.napier.ac.uk/Output/1792568