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Title: Predicting Pain: An Analysis of fMRI Data through Machine Learning Techniques
Authors: Chen, Amanda
Advisors: Liu, Han
Department: Operations Research and Financial Engineering
Class Year: 2015
Abstract: Recently, the increasing availability of fMRI data has allowed for better understanding and quantification of brain activity, with applications in predicting conditions such as neurological disease or pain. In this study, it is shown that classification of pain using support vector machine (SVM) yielded maximum accuracy rates of 92% and 87% when applied to two independent samples. Various feature elimination techniques were applied and assessed in order to reach optimal prediction accuracy. In addition, functional connectivity patterns in the two conditions were examined through correlation analysis. This analysis provides a step towards an accurate physiological determinant of pain, which is predominantly identified through self-report currently.
Extent: 53 pages
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Operations Research and Financial Engineering, 2000-2016

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