@inproceedings{15fab42d21ee47be863bf14d9512d0d0,
title = "Predicting obesity using longitudinal near infra-red spectroscopy (NIRS) data",
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 Na{\"i}ve Bayes modeling using these activation maps demonstrate how cerebral functional activity data can be used to predict obesity in the non-obese.",
keywords = "Data mining, NIRS, Na{\"i}ve Bayes, Obesity, Paired t-test, Prediction",
author = "Ahsan Abdullah and Amir Hussain and Khan, {Imtiaz Hussain}",
note = "Publisher Copyright: {\textcopyright} 2017 Copyright is held by the owner/author(s).; 2017 International Conference on Compute and Data Analysis, ICCDA 2017 ; Conference date: 19-05-2017 Through 23-05-2017",
year = "2017",
month = may,
day = "19",
doi = "10.1145/3093241.3093286",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "123--128",
booktitle = "Proceedings of 2017 International Conference on Compute and Data Analysis, ICCDA 2017",
}
