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J Neurol. 2019 Nov 1. doi: 10.1007/s00415-019-09604-6. [Epub ahead of print]

Machine-learning-derived rules set excludes risk of Parkinson's disease in patients with olfactory or gustatory symptoms with high accuracy.

Author information

1
Institute of Clinical Pharmacology, Goethe-University, Theodor Stern Kai 7, 60590, Frankfurt am Main, Germany. j.loetsch@em.uni-frankfurt.de.
2
Fraunhofer Institute of Molecular Biology and Applied Ecology-Project Group Translational Medicine and Pharmacology (IME-TMP), Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany. j.loetsch@em.uni-frankfurt.de.
3
Department of Otorhinolaryngology, Smell and Taste Clinic, TU Dresden, Fetscherstrasse 74, 01307, Dresden, Germany.

Abstract

BACKGROUND:

Chemosensory loss is a symptom of Parkinson's disease starting already at preclinical stages. Their appearance without an identifiable etiology therefore indicates a possible early symptom of Parkinson's disease. Supervised machine-learning was used to identify parameters that predict Parkinson's disease among patients having sought medical advice for chemosensory symptoms.

METHODS:

Olfactory, gustatory and demographic parameters were analyzed in 247 patients who had reported for chemosensory symptoms. Unsupervised machine-learning, implanted as so-called fast and frugal decision trees, was applied to map these parameters to a diagnosis of Parkinson's disease queried for in median 9 years after the first interview.

RESULTS:

A symbolic hierarchical decision rule-based classifier was created that comprised d = 5 parameters, including scores in tests of odor discrimination, odor identification and olfactory thresholds, the age at which the chemosensory loss has been noticed, and a familial history of Parkinson's disease. The rule set provided a cross-validated negative predictive performance of Parkinson's disease of 94.1%; however, its balanced accuracy to predict the disease was only 58.9% while robustly above guessing.

CONCLUSIONS:

Applying machine-learning techniques, a classifier was developed that took the shape of a set of six hierarchical rules with binary decisions about olfaction-related features or a familial burden of Parkinson's disease. Its main clinical strength lies in the exclusion of the possibility of developing Parkinson's disease in a patient with olfactory or gustatory loss.

KEYWORDS:

Data science; Decision trees; Machine-learning; Olfaction; Parkinson’s disease

PMID:
31676975
DOI:
10.1007/s00415-019-09604-6

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