Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification
المؤلفون المشاركون
Tahmassebi, Amirhessam
Gandomi, Amir H.
Schulte, Mieke H. J.
Goudriaan, Anna E.
Foo, Simon Y.
Meyer-Baese, Anke
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-24، 24ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-05-20
دولة النشر
مصر
عدد الصفحات
24
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1133350
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر