Date of Award
2013
Document Type
Thesis
Degree Name
Bachelors
Department
Natural Sciences
First Advisor
McDonald, Patrick
Keywords
Machine Learning, Neuroinformatics, Python
Area of Concentration
Natural Sciences
Abstract
Machine learning algorithms are often used on large biological data sets for the purposes of identifying biomarkers associated to disease which can be used as a diagnostic or prognostic tool. In this thesis, we demonstrate the classification accuracy of eight machine learning algorithms demonstrated on multiple schizophrenia and bipolar disorder related data sets. These algorithms include support vector machines, naive Bayes classifiers, and other clustering and regression techniques. All software used in the classification is open source to elucidate the potential of accessible and robust data mining software.
Recommended Citation
Rogers, Jack, "DIAGNOSING MENTAL ILLNESS USING MACHINE LEARNING" (2013). Theses & ETDs. 6789.
https://digitalcommons.ncf.edu/theses_etds/6789
Rights
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