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Sensors 2016, 16(9), 1498; doi:10.3390/s16091498

Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson’s Patients

1
Polish-Japanese Academy of Information Technology, 02-008 Warszawa, Poland
2
Department Neurology, University of Massachusetts Medical School, Worcester, MA 01655, USA
3
Mathematics and Statistics, Boston University, Boston, MA 02215, USA
4
Neurology, Faculty of Health Science, Medical University of Warsaw, Warszawa 03-242, Poland
*
Author to whom correspondence should be addressed.
Academic Editors: Miguel González-Mendoza, Ma. Lourdes Martínez-Villaseñor and Hiram Ponce
Received: 8 July 2016 / Revised: 29 August 2016 / Accepted: 31 August 2016 / Published: 14 September 2016
View Full-Text   |   Download PDF [1021 KB, uploaded 14 September 2016]   |  

Abstract

We still do not know how the brain and its computations are affected by nerve cell deaths and their compensatory learning processes, as these develop in neurodegenerative diseases (ND). Compensatory learning processes are ND symptoms usually observed at a point when the disease has already affected large parts of the brain. We can register symptoms of ND such as motor and/or mental disorders (dementias) and even provide symptomatic relief, though the structural effects of these are in most cases not yet understood. It is very important to obtain early diagnosis, which can provide several years in which we can monitor and partly compensate for the disease’s symptoms, with the help of various therapies. In the case of Parkinson’s disease (PD), in addition to classical neurological tests, measurements of eye movements are diagnostic. We have performed measurements of latency, amplitude, and duration in reflexive saccades (RS) of PD patients. We have compared the results of our measurement-based diagnoses with standard neurological ones. The purpose of our work was to classify how condition attributes predict the neurologist’s diagnosis. For n = 10 patients, the patient age and parameters based on RS gave a global accuracy in predictions of neurological symptoms in individual patients of about 80%. Further, by adding three attributes partly related to patient ‘well-being’ scores, our prediction accuracies increased to 90%. Our predictive algorithms use rough set theory, which we have compared with other classifiers such as Naïve Bayes, Decision Trees/Tables, and Random Forests (implemented in KNIME/WEKA). We have demonstrated that RS are powerful biomarkers for assessment of symptom progression in PD. View Full-Text
Keywords: neurodegenerative disease; rough set; machine learning; decision rules neurodegenerative disease; rough set; machine learning; decision rules
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Przybyszewski, A.W.; Kon, M.; Szlufik, S.; Szymanski, A.; Habela, P.; Koziorowski, D.M. Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson’s Patients. Sensors 2016, 16, 1498.

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