Reducing Intra-Subject Bias in Parkinson’s Disease Voice Classification with Ant Colony Optimization

• Pedro Lemes Sixel Lobo UFG
• Juliana Paula Felix UFG / PUC Goiás
• Rogerio Salvini UFG
• Clarimar Coelho PUC Goiás
• Fabrizzio Soares UFG

DOI: https://doi.org/10.5753/sbcas.2025.7506

Abstract

Parkinson’s Disease (PD) is a neurodegenerative condition that affects voice production. Previous studies have employed machine learning techniques for PD diagnosis based on voice data. However, the vast majority of existing approaches overlook intra-subject variation in their methodologies. This study proposes a methodology that accounts for such variation while also employing the bio-inspired Ant Colony Optimization algorithm for feature selection, thereby reducing overfitting. An accuracy of 84.80% was achieved in distinguishing individuals with PD from healthy controls, providing a fairer and reliable approach and highlighting the potential of bio-inspired algorithms in PD diagnosis.

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