
Robust Feature Engineering for Parkinson Disease Diagnosis: New Machine Learning Techniques
Author(s) -
Max Wang,
Wenbo Ge,
Deborah Apthorp,
Hanna Suominen
Publication year - 2020
Publication title -
jmir biomedical engineering
Language(s) - English
Resource type - Journals
ISSN - 2561-3278
DOI - 10.2196/13611
Subject(s) - phonation , computer science , support vector machine , feature (linguistics) , data set , set (abstract data type) , artificial intelligence , feature engineering , machine learning , pattern recognition (psychology) , deep learning , medicine , audiology , linguistics , philosophy , programming language
Background Parkinson disease (PD) is a common neurodegenerative disorder that affects between 7 and 10 million people worldwide. No objective test for PD currently exists, and studies suggest misdiagnosis rates of up to 34%. Machine learning (ML) presents an opportunity to improve diagnosis; however, the size and nature of data sets make it difficult to generalize the performance of ML models to real-world applications. Objective This study aims to consolidate prior work and introduce new techniques in feature engineering and ML for diagnosis based on vowel phonation. Additional features and ML techniques were introduced, showing major performance improvements on the large mPower vocal phonation data set. Methods We used 1600 randomly selected /aa/ phonation samples from the entire data set to derive rules for filtering out faulty samples from the data set. The application of these rules, along with a joint age-gender balancing filter, results in a data set of 511 PD patients and 511 controls. We calculated features on a 1.5-second window of audio, beginning at the 1-second mark, for a support vector machine. This was evaluated with 10-fold cross-validation (CV), with stratification for balancing the number of patients and controls for each CV fold. Results We showed that the features used in prior literature do not perform well when extrapolated to the much larger mPower data set. Owing to the natural variation in speech, the separation of patients and controls is not as simple as previously believed. We presented significant performance improvements using additional novel features (with 88.6% certainty, derived from a Bayesian correlated t test) in separating patients and controls, with accuracy exceeding 58%. Conclusions The results are promising, showing the potential for ML in detecting symptoms imperceptible to a neurologist.