| Abstract |
Parkinson’s disease is a deliberative neurogenetic disorder that occurs due to diagnostic challenge of the limitations in traditional methods. The symptoms of this disease cause slow down movement of muscles in humans, and it is also caused by the protein deficiency in the humans. This study explores the potential of the machine learning (ML) algorithms for identifying the Parkinson’s disease using voice analysis patterns in smart cities. By analyzing the variations in voice patterns, the proposed approach will aim to develop a cost-effective and efficient diagnostic tool. This research demonstrates promises for early detection and improved patient management of Parkinson’s disease. As computers cannot easily identify the disease prediction, by ML algorithms, we can find the prediction of disease with its accuracy. Recognizing the pressing need for a more accessible and efficient diagnostic tool, researchers and medical practitioners have turned to innovative approaches, leveraging advancements in ML algorithms. Through sophisticated algorithms and data analysis techniques, researchers aim to harness the potential of voice-based diagnostics, offering a faster and more cost-effective means of identifying Parkinson’s in its early stages. © 2025 selection and editorial matter, V. Subramaniyaswamy, G Revathy, Logesh Ravi, N. Thillaiarasu, and Naresh Kshetri; individual chapters, the contributors. |
