Parkinson Detection using Machine Learning Algorithms

Abstract

Abstract: Parkinson’s Disease (PD is an irreversible, chronic, and progressive disorder, estimated to affect about ten million people worldwide affecting patient movement and feelings of motivation and reward. Motor symptoms of PD include tremor or shaking, which usually begins in one arm or hand, muscle rigidity or stiffness, and slowness, also referred to as bradykinesia. Signs and symptoms are unique to each person and the treatment should be tailored to each person’s individual needs. A recent survey of 492 people with PD found most believed self-monitoring would improve their understanding of PD, well-being, ability to cope, and communication with their health-care team. The overlap of symptoms with other neurological disorders leads to difficulties, delays, and errors in diagnosis, negatively impacting patients. A recent study of 1775 patients’ experiences of PD diagnosis identified the importance of early diagnosis. and found misdiagnosis occurred at first consultation for 15% of participants. This fallibility of clinical diagnosis is recognized and requires periodic diagnosis reviews. Participants are presented with three activities evaluating Patients Call Response, Fine Motor Movements and Memory. To evaluated Patients, Name Call Response, the application uses a Chatbot to communicate with the patient, and tries to ask questions by calling his name. On successful response a series of other questions are asked on patients likes and dislikes. Fine Motor Deficit is evaluated based on a Finger Tapping Task, the patient as to tap on two buttons shown on the phone screen alternatively. The data is collected after 2 minutes on Tapping and compared with threshold set by the Normal Group. The Memory Activity measures the time taken by the patient to solve the puzzle by finding the corresponding images. Patients are shown images of fruits that are illuminated one at a time and then asked to find the same in the grid.