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Volume 2018, Article ID 2740817, 24 pages
https://doi.org/10.1155/2018/2740817
Research Article

Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification

1Department of Scientific Computing, Florida State University, Tallahassee, FL 32306-4120, USA
2School of Business, Stevens Institute of Technology, Hoboken, NJ 07030, USA
3Amsterdam Institute for Addiction Research, Department of Psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
4Addiction, Development, and Psychopathology (ADAPT) Lab, Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
5Department of Electrical and Computer Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310-6046, USA

Correspondence should be addressed to Amirhessam Tahmassebi; ude.usf@ibessamhata

Received 18 December 2017; Accepted 28 March 2018; Published 20 May 2018

Academic Editor: Danilo Comminiello

Copyright © 2018 Amirhessam Tahmassebi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction for nicotine-dependent patients and future treatment efficacy. Two classes of patients were studied. One class took the drug N-acetylcysteine and the other class took a placebo. Then, the patients underwent a double-blind smoking cessation treatment and the resting-state fMRI scans of their brains before and after treatment were recorded. The scientific research goal of this study was to interpret the fMRI connectivity maps based on machine learning algorithms to predict the patient who will relapse and the one who will not. In this regard, the feature matrix was extracted from the image slices of brain employing voxel selection schemes and data reduction algorithms. Then, the feature matrix was fed into the machine learning classifiers including optimized CART decision tree and Naive-Bayes classifier with standard and optimized implementation employing 10-fold cross-validation. Out of all the data reduction techniques and the machine learning algorithms employed, the best accuracy was obtained using the singular value decomposition along with the optimized Naive-Bayes classifier. This gave an accuracy of 93% with sensitivity-specificity of 99% which suggests that the relapse in nicotine-dependent patients can be predicted based on the resting-state fMRI images. The use of these approaches may result in clinical applications in the future.