• N. Navaneetha
• T. Suresh

Parkinson’s disease (PD) affects people’s movement, comprising changes in speech, writing ability, tremors, and stiffness in muscles. The PD’s important levels are quite dangerous as the patients become more and more severe outcomes in the incapability of walking or standing. Initial identification accompanied by correct medicine can lessen the tremors and discrepancy signs for patients, allowing them to lead an ordinary life. In the area of the healthcare industry, Machine learning (ML) and deep learning (DL) are being constantly applied to a kind of data condition, containing handwritten patterns and acoustic voice recording for PD diagnosis. This manuscript presents an Ensemble Model-based Parkinson’s Disease Diagnosis Using Feature Selection and Improved Pelican Optimization Algorithm (EMPDD-FSIPOA). This paper provides advanced deep learning and optimization algorithms for detecting PD in its early stages. Initially, the Z-score normalization has been used in the data pre-processing stage to transform input data into a beneficial design. For the feature selection (FS) method, the marriage in honey-bee optimizer (MBO) algorithm has been exploited. Furthermore, the proposed EMPDD-FSIPOA model executes ensemble of deep learning models namely variational autoencoder (VAE) method, temporal convolutional networks (TCN) model, and double deep Q networks (DDQN) technique for the classification process. At last, the parameter tuning process is performed through improved pelican optimization algorithm (IPOA) for developing the classification performance of the ensemble classifiers. The experimental assessment of the EMPDD-FSIPOA can be examined on a benchmark database. The widespread outcomes highlight the significant solution of the EMPDD-FSIPOA approach to the Parkinson’s disease classification process.

Ensemble Models; Parkinson’s disease Diagnosis; Feature Selection; Improved Pelican Optimization Algorithm; Data Preprocessing

"An Ensemble Models-based Parkinson’s Disease Diagnosis Using Feature Selection and Improved Pelican Optimization Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 8, page no.g52-g72, August-2025, Available :http://www.jetir.org/papers/JETIR2508610.pdf

"An Ensemble Models-based Parkinson’s Disease Diagnosis Using Feature Selection and Improved Pelican Optimization Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 8, page no. ppg52-g72, August-2025, Available at : http://www.jetir.org/papers/JETIR2508610.pdf