PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data
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Parkinson's disease is a neurodegenerative disease that can affect a person's movement, speech, dexterity, and cognition. Physicians primarily diagnose Parkinson's disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson's disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between healthy people and people with Parkinson's disease using long-term data collected from smartphone-based tests, including walking, voice, tapping and memory tests. We demonstrate that the presented approach leads to significant performance improvements over existing methods (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as additional evidence for the diagnosis of Parkinson's disease.
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