Beyond Traditional Machine Learning: Investigating Quantum Approaches to Parkinson's Disease Diagnosis

Parkinson's disease (PD) is a degenerative condition that affects the neurological structure and nerve-controlled components of the body. Timely intervention and individualized treatment of Parkinson's disease (PD) depend on an early and correct diagnosis. In recent times, there has been the emergence of potential methods for enhancing PD diagnosis, including Machine Learning (ML), Deep Learning (DL), and Quantum Machine Learning (QML). This work provides a detailed analysis of the state of machine learning, deep learning and quantum machine learning based Parkinson's disease diagnosis. It focuses on Magnetic resonance imaging (MRI), handwriting, voice, and wave spiral datasets. Previous research has some restrictions and difficulties, but these can be addressed by using quantum machine learning on the Parkinson's dataset, which can be based on both non-motor and motor symptoms. According to this study, detecting Parkinson's disease with quantum machine learning will require specialized knowledge and additional resources, which will be highly expensive. In future, we can use machine learning techniques, like ensemble approaches, as the conclusion suggests and with the use of performance indicators such as F-Beta score, recall, accuracy, and precision, etc., we can improve the model's correctness. By providing guidance for the creation of ML and DL-based methods for PD identification, this study acts as a road map for future research. As we gain a better understanding of PD diagnosis and eventually improve patient outcomes, it is anticipated to be beneficial to both the scientific community and medical professionals.