IoT-based Cyber-Physical System for Parkinson Inmates Using ML Prediction Algorithms

Most people in the eternal world we reawaken to are stressed and wrecked with diseases because of genetic, environmental, and emotional causes. There are many incurable diseases, but there are also ways to treat them that are hopeful and even life-extending. As a result, it is hypothesized that the prototype will limit the capacities of people with Parkinson's disease. It's a disease that causes degeneration in the brain and spinal cord. The most noticeable signs are trembling, stiffness, slowness of movement, and difficulty walking. Depression and anxiety are symptoms of dementia, a form of brain condition that worsens as a result of this disease. It also makes it hard to get to sleep at night. For some illnesses, we lack the diagnostic instrument necessary to determine the precise status of many conditions. Wearable sensors can diagnose Parkinson's disease (PD) in its earliest stages, when treatment is most effective. Brain wave detection and other forms of human chronicle are detected with effective sensors in the proposed system for Parkinson disease, demonstrating the improvement of the patient's central nervous system. Here, we use machine learning with a prediction algorithm to keep a close eye on the patient's progress and store the data in the cloud automatically so that we can reliably get an analysis of the patient's performance at any time. For better classification results, we create a prediction system based on the fuzzy k-nearest neighbor (FKNN). The suggested system may monitor patients in more ways than just by measuring vitals like heart rate, blood pressure, and temperature.