Locally Interpretable Predictions of Parkinson's Disease Progression

In precision medicine, machine learning techniques have been commonly proposed to aid physicians in early screening of chronic diseases. Many of these diseases become more difficult to treat as they progress, so accurate early screening is critical to ensure resources are directed towards the most effective treatment plan [Pagan, 2012]. Since the final treatment decision must inevitably be made by a doctor, these screening procedures should be interpretable such that a clinician can explain the decision-making process to patients for informed consent. However, the types of models that achieve the highest level of accuracy given early screening data tend to be extremely complex, meaning that even machine learning experts have difficulties explaining why certain predictions are made, leading many to describe them as "black box" [Breiman, 2001]. In this paper, we bridge this gap by providing a novel approach for explaining black box model predictions which can give high fidelity explanations with lower model complexity. In particular we will focus on early screening of Parkinson's Disease (PD). PD is a complicated neurodegenerative disorder that affects the central nervous system and specifically the motor control of individuals [mjf, 2019]. This disorder is estimated to affect 930,000 individuals in the US by 2020, and is more prevalent in the geriatric population affecting more then 1% of the population over the age of 60 and 5% of the population over age 85 [Findley, 2007, Kowal et al., 2013, Rossi et al., 2018]. These statistics and other recent studies on Parkinson's epidemiology indicate that as the population ages, the prevalence of PD is expected to grow to over 1.2 million by 2030 in the US alone, increasing the total economic burden of the disorder to approximately $26 billion USD [Kowal et al., 2013, Rossi et al., 2018].