Voice-driven Parkinson’s disease prediction using a chaotic Grey Wolf–Dragonfly algorithm in high-dimensional datasets

Abstract Parkinson’s Disease (PD) is a progressive neurological disorder affecting motor and non-motor functions. Early detection significantly improves patient outcomes, yet traditional clinical diagnoses are often delayed. Machine learning (ML), especially using speech data—given that over 90% of PD patients experience speech impairments—offers a promising alternative for early diagnosis. However, the high dimensionality of PD datasets poses challenges for prediction accuracy, highlighting the need for effective feature selection. This study proposes a novel hybrid feature selection method, the Chaotic Grey Wolf–Dragonfly Algorithm (CGWO-DA), which integrates the Grey Wolf Optimizer (GWO), Dragonfly Algorithm (DA), and a Logistic Chaotic Map to improve the balance between exploration and exploitation and prevent premature convergence. CGWO-DA was applied to three PD speech datasets of varying sizes. Preprocessing steps included label encoding, normalization, and irrelevant column removal, followed by an 80–20 training-test data split. CGWO-DA outperformed traditional methods, selecting optimal features and improving classifier performance. On a small dataset, it achieved 100% accuracy using Random Forest with 13 selected features. On medium and large datasets, it achieved 90% and 96% accuracy using Deep Neural Networks and Random Forest, respectively. These findings highlight CGWO-DA’s effectiveness and its potential for broader application, including the diagnosis of non-motor PD symptoms.