Parkinson’s Disease detection

The goal of this project is to develop a machine learning model that can assist in the detection of Parkinson's Disease (PD) from medical data, such as speech recordings, motor activity, or clinical examination results. Parkinson’s Disease is a progressive neurodegenerative disorder that affects movement, causing symptoms such as tremors, stiffness, and slowness of movement. Early detection is crucial for slowing disease progression and improving patient outcomes, and machine learning can significantly aid in diagnosing PD with non-invasive methods.

The primary objective is to develop an algorithm that can identify Parkinson’s Disease from a dataset (such as speech or motor performance data), helping healthcare professionals with early diagnosis and better management of the condition.