Predicting Early Stage Drug Induced Parkinsonism Using Unsupervised And Supervised Machine Learning

Drug Induced Parkinsonism (DIP) is the most common, debilitating movement disorder induced by antipsychotics. There is no tool available in clinical practice to effectively diagnose the symptoms at the onset of the disease. In this study, the variations in gait accelerometer data due to the intermittency of tremor at the initial stages is examined. These variations are used to train a logistic regression model to predict subjects with early-stage DIP. The logistic classifier predicts if a subject is a DIP or control with approximately 89% sensitivity and 96% specificity. This paper discusses the algorithm used to extract the features in gait data for training the classifier to predict DIP at the earliest.