Optimized naive-Bayes and decision tree approaches for fMRI smoking cessation classification

DOI10.1155/2018/2740817zbMath1390.92074OpenAlexW2803106131WikidataQ60539961 ScholiaQ60539961MaRDI QIDQ1649494

Anna E. Goudriaan, Amirhessam Tahmassebi, Mieke H. J. Schulte, Simon Y. Foo, Anke Meyer-Baese, Amir Hossein Gandomi

Publication date: 6 July 2018

Published in: Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2018/2740817

zbMATH Keywords

machine learning algorithmsfMRI scansfMRI connectivity mapsfuture treatment efficacyNaive-Bayes classifiernicotine-dependent patientsoptimized CART decision treetheory-driven biomarkers

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Medical applications (general) (92C50)

Uses Software

• Scikit
• SLIQ

Cites Work

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• Learning to decode cognitive states from brain images
• Independent component analysis, a new concept?
• Functional Causal Mediation Analysis With an Application to Brain Connectivity
• The elements of statistical learning. Data mining, inference, and prediction

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