Pérez-Sánchez, BeatrizSánchez-Maroño, NoeliaDíaz-Freire, Miguel A.2025-12-162025-12-162024-05Beatriz Pérez-Sánchez, Noelia Sánchez-Maroño, and Miguel A. Díaz-Freire. 2024. Analysis of voice recordings features for Classification of Parkinson's Disease. In Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing (SAC '24). Association for Computing Machinery, New York, NY, USA, 531–532. https://doi.org/10.1145/3605098.3636135979-8-4007-0243-3https://hdl.handle.net/2183/46666Presented at: SAC '24: 39th ACM/SIGAPP Symposium on Applied Computing, Avila Spain, April 8 - 12, 2024. "This is the author's version of the work. The definitive Version of Record was published in Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing (SAC '24). Association for Computing Machinery, New York, NY, USA, 531–532. https://doi.org/10.1145/3605098.3636135"[Abstract]: Parkinson's disease (PD) is a chronic neurodegenerative disease. Motor symptoms are very mild in the early stages, making diagnosis difficult. Recent studies have shown that the use of patient voice recordings can aid in early diagnosis. Although the analysis of such recordings is costly from a clinical perspective, machine learning techniques are making their processing increasingly accurate and efficient. Voice recordings contain many features, but it is unknown which ones are relevant to the diagnosis of this disease. This paper proposes the use of machine learning models combined with feature selection methods for the classification of PD patients. The results show that this approach is appropriate since it drastically reduces the number of features maintaining high classification performance.engCopyright © 2024 Copyright held by the owner/author(s). Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).Parkinson diseaseArtificial neural networksSupport vector machinesVoice recordingsComputing methodologiesFeature selectionApplied computingHealth informaticsNeural networksconference outputopen access10.1145/3605098.3636135