QFLPD: Quantum Fuzzy Logic for Prediction of the Parkinson Disease

Parkinson's disease (PD) is described as a chronic neurodegenerative condition that affects movement, causing degeneration of the neurons that produce dopamine in the brain. This degeneration is expressed through motor symptoms like tremors, bradykinesia (movement reduction), rigidity, and postural instability, as well as non-motor symptoms like cognitive impairment, depression, and sleep disturbances. The impact of PD on those affected is considerable, often leading to a notable decline in quality of life, diminished independence, and heightened healthcare costs. Although therapeutic approaches like dopamine replacement therapy, deep brain stimulation, and rehabilitation can alleviate symptoms, identifying a conclusive treatment for PD remains a challenging endeavor. Accurate and careful analysis is essential for effective treatment planning and for slowing down the disease's progression. Fuzzy logic, which addresses uncertainty and vague information, can predict PD by examining the severity of symptoms and patient information. By combining quantum computing with advanced data analysis, we can significantly improve the way we process and understand complex medical data. This breakthrough enables us to make more accurate predictions about the progression of diseases, such as Parkinson's, and even offers the potential for personalized treatment recommendations. By harnessing the power of quantum computing, can tap into the intricate patterns hidden within neurological signals, ultimately leading to more effective treatments for patients with Parkinson's and other conditions. The proposed model attains 95% in accurately predicting the risk.