Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification
Joint Authors
Tahmassebi, Amirhessam
Gandomi, Amir H.
Schulte, Mieke H. J.
Goudriaan, Anna E.
Foo, Simon Y.
Meyer-Baese, Anke
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-24, 24 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-05-20
Country of Publication
Egypt
No. of Pages
24
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Tahmassebi, Amirhessam& Gandomi, Amir H.& Schulte, Mieke H. J.& Goudriaan, Anna E.& Foo, Simon Y.& Meyer-Baese, Anke. 2018. Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification. Complexity،Vol. 2018, no. 2018, pp.1-24.
https://search.emarefa.net/detail/BIM-1133350
Modern Language Association (MLA)
Tahmassebi, Amirhessam…[et al.]. Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification. Complexity No. 2018 (2018), pp.1-24.
https://search.emarefa.net/detail/BIM-1133350
American Medical Association (AMA)
Tahmassebi, Amirhessam& Gandomi, Amir H.& Schulte, Mieke H. J.& Goudriaan, Anna E.& Foo, Simon Y.& Meyer-Baese, Anke. Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification. Complexity. 2018. Vol. 2018, no. 2018, pp.1-24.
https://search.emarefa.net/detail/BIM-1133350
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1133350