Enhancing Early Detection of Parkinson’s Disease Through Ensemble Learning and Nature‐Inspired Feature Selection

Parkinson’s disease (PD) is a common central nervous system disorder that affects patients’ speech abilities. Early detection of PD is essential for improving the treatment process. In the past decade, extensive research has been conducted on the early diagnosis of PD using artificial intelligence and machine learning methods. Feature selection and ensemble learning have shown to be effective strategies for designing diagnostic models. This study proposes an ensemble learning model combining support vector machine (SVM) and random forest (RF) algorithms to enhance the learning process. To reduce dimension and further improve the learning process, we employ nature‐inspired meta‐heuristic algorithms, such as binary particle swarm optimization (BPSO), binary butterfly optimization algorithm (BBOA), and binary whale optimization algorithm (BWOA) for feature selection. We utilized the standard PD dataset from the UCI database for our experiments. Our results demonstrate that the combination of BWOA‐based feature selection and ensemble classifier achieves the highest performance, with an impressive accuracy of 97.43%. The proposed method effectively combines the strengths of BWOA for feature selection and ensemble learning for PD identification, resulting in exceptional accuracy and showcasing its potential for early diagnosis and treatment of the disease. The primary limitations of this study include its dependence on data quality, potential computational complexity, and the need for further validation across diverse populations.