Parkinson’s Disease Prediction Using Machine Learning Models

Parkinson's disease is a neurodegenerative condition that affects billions of persons worldwide. This abstract aims to shed light on the causes and consequences of this debilitating condition. The primary cause of Parkinson's disease is the progressive degeneration of dopaminergic neurons in the substantia nigra region of brain. This neuronal loss results in a depletion of dopamine, a crucial neurotransmitter responsible for regulating movement and coordination. Therefore, individuals with Parkinson's disease have symptoms like tremors, rigidity, bradykinesia, and postural instability. These signs profoundly impact the quality of life, causing difficulties with daily activities and reducing independence. In addition to motor symptoms, non-motor symptoms such as depression, cognitive impairment, and autonomic dysfunction often accompany the disease, further complicating the clinical picture. Research into the causes and consequences of Parkinson's disease is ongoing, with a focus on using efficient medications and refining the quality of life for those affected by this condition. Now by Using machine learning algorithms, we can predict whether a person has a specific disease based on input values like gender and age. These algorithms analyze patterns and relationships in data to get predictions about an individual's health status. This technology can assist in early disease detection and improve healthcare outcomes..