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Although the preclinical detection of Parkinson's disease (PD) has been explored, a practical, inexpensive, and overall screening diagnosis has yet to be made available. However, due to the large variability and complexity in progress of PD and the difficulties in gathering a single time-point measurement of a single sign, the goal of precision treatment and assessment severity would be impossible to achieve. Hence, the repeated monitoring and tracking of individuals during their daily living activities at different times would also be of great importance for treating this chronic disease. We propose a deep multi-layer perceptron (DMLP) classifier for behavior analysis to estimate the progression of PD using smartphones. This paper aims to identify severity in PD patients' actions by analyzing their speech and movement patterns, as measured with a smartphone accelerometer in their pocket at different times of the day. Popular machine learning classification algorithms, such as logistic regression, random forests, k-nearest neighbors, M5P, and DMLP, are applied on one dataset from the University of California Irvine and another dataset collected by the authors to classify each patient as being Parkinson positive or negative. We further measure the success of each method for their ability to correctly classify the patients into one of these categories. Of the experimental models, it is demonstrated that DMLP performs the best in both datasets.

Deep Multi-Layer Perceptron Classifier for Behavior Analysis to Estimate Parkinson’s Disease Severity Using Smartphones