Using partial decision trees to predict Parkinson's symptoms: A new approach for diagnosis and therapy in patients suffering from Parkinson's disease

Authors:
Themis P. Exarchos;Alexandros T. Tzallas;Dina Baga;Dimitra Chaloglou;Dimitrios I. Fotiadis;Sofia Tsouli;Maria Diakou;Spyros Konitsiotis
Affiliations:
Unit of Medical Technology and Intelligent Information System, Department of Materials Science and Engineering, University of Ioannina, GR 45110, Ioannina, Greece;Unit of Medical Technology and Intelligent Information System, Department of Materials Science and Engineering, University of Ioannina, GR 45110, Ioannina, Greece;Unit of Medical Technology and Intelligent Information System, Department of Materials Science and Engineering, University of Ioannina, GR 45110, Ioannina, Greece;ANCO S.A., Athens GR 11742, Greece;Unit of Medical Technology and Intelligent Information System, Department of Materials Science and Engineering, University of Ioannina, GR 45110, Ioannina, Greece;Dept. of Neurology, Medical School, University of Ioannina, GR 45110, Ioannina, Greece;Dept. of Neurology, Medical School, University of Ioannina, GR 45110, Ioannina, Greece;Dept. of Neurology, Medical School, University of Ioannina, GR 45110, Ioannina, Greece
Venue:
Computers in Biology and Medicine
Year:
2012

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Abstract

In this work we present a method based on partial decision trees and association rules for the prediction of Parkinson's disease (PD) symptoms. The proposed method is part of the PERFORM system. PERFORM is used for the treatment of PD patients and even advocate specific combinations of medications. The approach presented in this paper is included in the data miner module of PERFORM. A patient performs some initial examinations and the module predicts the future occurrence of the symptoms based on the initial examinations and medications taken. Using the method, the expert can prescribe specific medications that will not cause, or postpone the appearance of specific symptoms to the patient. The approach employed is able to provide interpretation for the predictions made, by providing rules. The models have been developed and evaluated using real patient's data and the respective results are reported. Another functionality of the data miner module is the extraction of rules through a user friendly interface using association rule mining algorithms. These rules can be used for the prediction analysis of patient's reaction to certain treatment plans. The accuracy of the symptoms' prediction ranges from 57.1 to 77.4%, depending on the symptom.