1 College of Medicine, Isabela State University, Echague Campus, Echague Isabela, Philippines.
2 School of Information Technology, MAPUA University, Makati Campus, Makati, Philippines.
3 College of Information Technology, Northeastern College, Santiago City, Isabela, Philippines.
4 Philippine Normal University, Alica, Isabela, Philippines.
5 College of Computing Studies, Information and Communication Technology, Isabela State Univeirsty, Cauayan Campus, Cauayan City, Isabela.
World Journal of Advanced Research and Reviews, 2025, 25(02), 2127-2133
Article DOI: 10.30574/wjarr.2025.25.2.0595
Received on 12 January 2025; revised on 23 February 2025; accepted on 26 February 2025
Parkinson's disease (PD) is a progressive neurological condition that impairs motor and speech function. Early and precise detection is critical for prompt intervention and illness treatment. This work applies machine learning approaches to classify Parkinson's disease using speech biomarkers collected from voice recordings. The dataset includes a variety of acoustic parameters that capture speech anomalies often seen in people with Parkinson's disease. The Chi-Square (Chi2) approach was used to pick the most important predictors, which improved model performance and reduced computational complexity. The fine K-Nearest Neighbors (KNN) classifier was implemented, achieving a validation accuracy of 74.7%. The model demonstrated a moderate ability to distinguish between Parkinson’s and non-Parkinson’s cases, as indicated by an area under the curve (AUC) score of 0.7421. However, the confusion matrix revealed challenges in misclassification, with false positives leading to potential unnecessary medical evaluations and false negatives resulting in missed diagnoses. This study highlights the potential of machine learning in Parkinson’s detection while emphasizing the need for further refinement to enhance classification accuracy
Parkinsons; Machine Learning; Feature Importance; Neurology
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Generaldo Maylem, Genica Lynne Maylem, Isaac Angelo M. Dioses, Loida Hermosura, James Bryan Tababa, Aldrin Bryan Tababa, Marc Zenus Labuguen and Dave Miracle Cabanilla. Speech-based biomarkers for Parkinson’s disease detection and classification using AI Approach. World Journal of Advanced Research and Reviews, 2025, 25(02), 2127-2133. Article DOI: https://doi.org/10.30574/wjarr.2025.25.2.0595.
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0