Development of Web Platform for Home Screening of Neurological Disorders Using Artificial Intelligence

Millions of people worldwide suffer with Parkinson's disease (PD), one of the most prevalent neurological illnesses. The diagnosis of PD has changed significantly over the last 25 years; at first, autopsy and neurological exams were the only methods accessible. Impaired handwriting has been shown to be a significant early indicator for this disease's identification. The purpose of this study was to use machine learning techniques to provide a quick and easy method of differentiating PD patients from controls using handwriting activities. It developed a telemonitoring technology based on user-friendly drawing tablet software that gave the subject visual feedback on the screen and enabled us to gather data in real time about the location, pressure, and tilt of the digital pen throughout the procedure. This paper created a procedure that included writing and drawing exercises, including ones in Italian, and we gathered information from ten PD patients and twenty healthy participants. With a 93.4% accuracy rate, the current research developed a machine learning (ML) framework that automatically separates PD patients from healthy control people using signals and data from a previous database.