Parkinson’s Disease Detection – A neurogenerative motor disorder detection using KSVM

Parkinson's disease is a chronic and progressive neurological disorder that affects movement and often leads to severe motor impairments such as tremors, stiffness, slowness, and instability. Early detection of the disease is critical to initiating timely treatment, which can help slow its progression and improve the quality of life for affected individuals. This project focuses on leveraging machine learning to build an accurate and reliable detection system that identifies early signs of Parkinson's disease. The proposed system involves collecting and analyzing datasets containing motor and non-motor symptoms to train classification models capable of distinguishing between healthy individuals and those with Parkinson's disease. Advanced feature extraction techniques and algorithms are employed to enhance model performance. Additionally, the project emphasizes accessibility by developing a tool that can be easily deployed in healthcare settings or integrated into mobile/desktop platforms for remote monitoring and diagnosis. By providing a cost-effective and scalable solution, this project aims to empower healthcare professionals with an efficient diagnostic tool, raise awareness about Parkinson's disease, and ultimately contribute to improving patient care and disease management outcomes.