Detecting Parkinson's Disease Using Artificial Neural Networks:Enhancing Early Diagnosis through Machine Learning

This study explores the application of Artificial Neural Networks for the detection anddiagnosis of Parkinson's Disease. Parkinson's Disease is a progressiveneurodegenerative disorder characterized by motor and non-motor symptoms thatseverely affect patients' quality of life. Traditional diagnostic methods rely heavily onclinical evaluations, which can be subjective and may delay diagnosis. Our researchemploys ANNs to improve early detection accuracy by analyzing various datasets,including voice and gait parameters. The ANN models used in this study demonstratedhigh accuracy rates in distinguishing PD patients from healthy controls. The resultsindicate that ANNs can effectively identify subtle patterns and features associated withPD, providing a more objective and timely diagnostic tool. This has significantimplications for improving patient outcomes through earlier and more accuratedetection. Future research should focus on expanding the dataset and integratingmultimodal data to enhance the robustness and applicability of ANN-based diagnosticsin clinical settings.