Applying Data Mining and Machine Learning Algorithms to predict symptom development in Parkinson's disease
Journal Title: Annales Academiae Medicae Silesiensis - Year 2014, Vol 68, Issue 5
Abstract
The standard treatment of PD symptoms depends on the experience of a particular neurologist, UPDRS and Hoehn and Yahr scale measurements in order to estimate the stage of PD, the patient’s reports and patient’s responses to medications. All these estimations are to a great extent subjective and determine different treatments in different centers. The purpose of this work was to develop an approach that may more precisely and objectively estimate a patient’s symptoms and in consequence optimize individual PD treatment. We have presented several examples of different methods that make measurements in PD more precise. However, greater precision and objectivity were only the first steps. In addition, all (standard and new) data must be evaluated in an intelligible way in order to better estimate PD symptoms and their developments. We have used data mining and machine learning approaches to mimic the “golden” neurologist’s reasoning.
Authors and Affiliations
Andrzej Przybyszewski
Keywords
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