Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification
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-ac...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/2740817 |
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| author | Amirhessam Tahmassebi Amir H. Gandomi Mieke H. J. Schulte Anna E. Goudriaan Simon Y. Foo Anke Meyer-Baese |
| author_facet | Amirhessam Tahmassebi Amir H. Gandomi Mieke H. J. Schulte Anna E. Goudriaan Simon Y. Foo Anke Meyer-Baese |
| author_sort | Amirhessam Tahmassebi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c0aa384fd0924e19a75eb9b2e18b6ed2 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c0aa384fd0924e19a75eb9b2e18b6ed22025-02-03T01:12:27ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/27408172740817Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation ClassificationAmirhessam Tahmassebi0Amir H. Gandomi1Mieke H. J. Schulte2Anna E. Goudriaan3Simon Y. Foo4Anke Meyer-Baese5Department of Scientific Computing, Florida State University, Tallahassee, FL 32306-4120, USASchool of Business, Stevens Institute of Technology, Hoboken, NJ 07030, USAAmsterdam Institute for Addiction Research, Department of Psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, NetherlandsAmsterdam Institute for Addiction Research, Department of Psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, NetherlandsDepartment of Electrical and Computer Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310-6046, USADepartment of Scientific Computing, Florida State University, Tallahassee, FL 32306-4120, USAThis 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.http://dx.doi.org/10.1155/2018/2740817 |
| spellingShingle | Amirhessam Tahmassebi Amir H. Gandomi Mieke H. J. Schulte Anna E. Goudriaan Simon Y. Foo Anke Meyer-Baese Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification Complexity |
| title | Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification |
| title_full | Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification |
| title_fullStr | Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification |
| title_full_unstemmed | Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification |
| title_short | Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification |
| title_sort | optimized naive bayes and decision tree approaches for fmri smoking cessation classification |
| url | http://dx.doi.org/10.1155/2018/2740817 |
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