Exploration of machine learning techniques in predicting multiple sclerosis disease course

To explore the value of machine learning methods for predicting multiple sclerosis disease course. 1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vector machines (SVM) were used to build the classif...

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Veröffentlicht in:	PloS one 2017-04, Vol.12 (4), p.e0174866-e0174866
Hauptverfasser:	Zhao, Yijun, Healy, Brian C, Rotstein, Dalia, Guttmann, Charles R G, Bakshi, Rohit, Weiner, Howard L, Brodley, Carla E, Chitnis, Tanuja
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Analysis Artificial intelligence Autoimmune diseases Biology and Life Sciences Brain Brain - diagnostic imaging Brain - pathology Classification Computer and Information Sciences Diagnosis Disease Progression Female Genetics Humans Learning Learning algorithms Logistic Models Machine Learning Magnetic Resonance Imaging Male Medicine and Health Sciences Multiple sclerosis Multiple Sclerosis - pathology Neuroimaging Neurology Patients People and Places Physical Sciences Predictions Research and Analysis Methods Sensitivity Sensitivity and Specificity Short term Statistical analysis Support Vector Machine Support vector machines
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