Hybrid Metaheuristics with Deep Learning-Assisted Parkinson’s Disease Detection and Classification
| International Journal of Electronics and Communication Engineering |
| © 2025 by SSRG - IJECE Journal |
| Volume 12 Issue 4 |
| Year of Publication : 2025 |
| Authors : N. Navaneetha, T. Suresh, V. Sathiyasuntharam |
10.14445/23488549/IJECE-V12I4P106
How to Cite?
N. Navaneetha, T. Suresh, V. Sathiyasuntharam, "Hybrid Metaheuristics with Deep Learning-Assisted Parkinson’s Disease Detection and Classification," SSRG International Journal of Electronics and Communication Engineering, vol. 12, no. 4, pp. 62-74, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I4P106
Abstract:
Parkinson's Disease (PD) is a chronic neurological disorder that advances gradually, with signs often resembling those of other conditions. Timely detection and diagnosis of PD are crucial for giving the appropriate treatment, assisting patients in maintaining their health and improving their quality of life. These disease signs have been described as slowness in activities, muscle rigidity, tremors, and balancing with other psychiatric signs. Handwritten heath records are the main devices that support PD recognition and evaluation. Many Machine Learning (ML) methodologies have been discovered for the early recognition of PD. However, many handcrafted feature extractor techniques mainly suffer from lower-performance accuracy problems. Therefore, Deep Learning (DL) models are widely used to analyse medical data. In this view, this study presents a Hybrid Meta-heuristics with DL Assisted PD Detection and Classification (HMDL-PDDC) technique. The HMDL-PDDC technique follows the hybrid metaheuristics-based Feature Selection (FS) design with an optimum DL method for recognizing and identifying PD. In the HMDL-PDDC technique, feature subsets are selected using an Improved Salp Swarm Algorithm (ISSA). Besides, the Kernel-based Deep Elman Neural Networks (KDENNs) technique is exploited to detect and identify PD. Moreover, the hyperparameter selection of the KDENN model is performed by an Object‐Oriented Programming Optimization Algorithm (OOPOA) technique. The experimentation outcomes of the HMDL-PDDC model are examined under four datasets using a set of measures. The experimental assessment of the HMDL-PDDC technique illustrated superior accuracy values of 93.98%, 94.97%, 98.71% and 97.10% over existing models.
Keywords:
Parkinson’s Disease, Metaheuristics, Deep learning, Hyperparameter tuning, Salp Swarm algorithm.
References:
[1] Amin Ul Haq et al., “Feature Selection Based on L1-Norm Support Vector Machine and Effective Recognition System for Parkinson’s Disease Using Voice Recordings,” IEEE Access, vol. 7, pp. 37718-37734, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Ozkan Cigdem, and Hasan Demirel, “Performance Analysis of Different Classification Algorithms Using Different Feature Selection Methods on Parkinson's Disease Detection,” Journal of Neuroscience Methods, vol. 309, pp. 81-90, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Gunjan Pahuja, and T. N. Nagabhushan, “A Comparative Study of Existing Machine Learning Approaches for Parkinson's Disease Detection,” IETE Journal of Research, vol. 67, no. 1, pp. 4-14, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Liaqat Ali et al., “Early Diagnosis of Parkinson’s Disease from Multiple Voice Recordings by Simultaneous Sample and Feature Selection,” Expert Systems with Applications, vol. 137, pp. 22-28, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Amira S. Ashour et al., “A Novel Framework of Two Successive Feature Selection Levels Using Weight-Based Procedure for Voice-Loss Detection in Parkinson’s Disease,” IEEE Access, vol. 8, pp. 76193-76203, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Gabriel Solana-Lavalle, and Roberto Rosas-Romero, “Classification of PPMI MRI Scans with Voxel-based Morphometry and Machine Learning to Assist in the Diagnosis of Parkinson’s Disease,” Computer Methods and Programs in Biomedicine, vol. 198, pp. 1-15, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Rohit Lamba, Tarun Gulati, and Anurag Jain, “Comparative Analysis of Parkinson’s Disease Diagnosis System,” Advances in Mathematics: Scientific Journal, vol. 9, no. 6, pp. 3399-3406, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] C. Okan Sakar et al., “A Comparative Analysis of Speech Signal Processing Algorithms for Parkinson’s Disease Classification and the Use of the tunable Q-Factor Wavelet Transform,” Applied Soft Computing, vol. 74, pp.255-263, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Iqra Nissa et al., “Voice-Based Detection of Parkinson’s Disease through Ensemble Machine Learning Approach: A Performance Study,” EAI Endorsed Transactions on Pervasive Health and Technology, vol. 5, no. 19, pp. 1-8, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Rohit Lamba et al., “A Hybrid System for Parkinson’s Disease Diagnosis using Machine Learning Techniques,” International Journal of Speech Technology, vol. 25, pp. 583-893, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] M.K. Dharani, and R.B. Thamilselvan, “Hybrid Optimization Enabled Deep Learning Model for Parkinson's Disease Classification, The Imaging Science Journal, vol. 72, no. 2, pp. 167-182, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Mehedi Masud et al., “CROWD: Crow Search and Deep Learning based Feature Extractor for Classification of Parkinson’s Disease,” ACM Transactions on Internet Technology, vol. 21, no. 3, pp. 1-18, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Adel A. Bahaddad et al., “Metaheuristics with Deep Learning-Enabled Parkinson’s Disease Diagnosis and Classification Model,” Journal of Healthcare Engineering, vol. 2022, pp. 1-14, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Puppala Praneeth et al., “Classification of Parkinson's Disease in Brain MRI Images Using Deep Residual Convolutional Neural Network (DRCNN),” International Journal of Computer Information Systems and Industrial Management Applications, vol. 15, pp. 383-395, 2023.
[Google Scholar] [Publisher Link]
[15] Feng Chen, Chunyan Yang, and Mohammad Khishe, “Diagnose Parkinson’s Disease and Cleft Lip and Palate Using Deep Convolutional Neural Networks Evolved by Ip-Based Chimp Optimization Algorithm,” Biomedical Signal Processing and Control, vol. 77, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] S. Pragadeeswaran, and S. Kannimuthu, “An Adaptive Intelligent Polar Bear (AIPB) Optimization-Quantized Contempo Neural Network (QCNN) Model for Parkinson’s Disease Diagnosis using Speech Dataset,” Biomedical Signal Processing and Control, vol. 87, no. 2, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[17] S. Sharanyaa, M. Sambath, and P. N. Renjith, “Optimized Deep Learning for the Classification of Parkinson's Disease Based on Voice Features,” Critical Reviews™ in Biomedical Engineering, vol. 50, no. 5, 2022.
[Google Scholar] [Publisher Link]
[18] Huimin Lu et al., “A Novel Feature Extraction Method Based on Dynamic Handwriting for Parkinson’s Disease Detection,” PloS One, vol. 20, no. 1, pp. 1-26, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Babita Majhi et al., “An Improved Method for Diagnosis of Parkinson’s Disease Using Deep Learning Models Enhanced with Metaheuristic Algorithm,” BMC Medical Imaging, vol. 24, no. 1, pp. 1-20, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Shraddha Jain, and Rajeev Srivastava, “Electroencephalogram (EEG) Based Fuzzy Logic and Spiking Neural Networks (FLSNN) for Advanced Multiple Neurological Disorder Diagnosis,” Brain Topography, vol. 38, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Siamak Hadadi, and Soodabeh Poorzaker Arabani, “A Novel Approach for Parkinson’s Disease Diagnosis Using Deep Learning and Harris Hawks Optimization Algorithm with Handwritten Samples,” Multimedia Tools and Applications, vol. 83, pp. 81491-81510, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] S. Kumar Reddy Mallidi, and Rajeswara Rao Ramisetty, “Bowerbird Courtship-Inspired Feature Selection for Efficient High-Dimensional Data Analysis Using a Novel Meta-Heuristic,” Discover Computing, vol. 28, pp. 1-24, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Aleksa Cuk et al., “Tuning Attention based Long-Short Term Memory Neural Networks for Parkinson’s Disease Detection Using Modified Metaheuristics,” Scientific Reports, vol, 14, pp. 1-12, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Serdar Ekinci et al., “Novel Application of Sinh Cosh Optimizer for Robust Controller Design in Hybrid Photovoltaic-Thermal Power Systems,” Scientific Reports, vol. 15, pp. 1-14, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Jelica Cincovic et al., “Neurodegenerative Condition Detection Using Modified Metaheuristic for Attention Based Recurrent Neural Networks and Extreme Gradient Boosting Tuning,” IEEE Access, vol. 12, pp. 26719-26734, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[26] K.V. Siva Prasad Reddy, and Meera Selvakumar, “Personalized Recommendation System to Handle Skin Cancer at Early Stage Based on Hybrid Model,” Network: Computation in Neural Systems, pp.1-40, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Aktham Sawan et al., “Hybrid Deep Learning and Metaheuristic Model Based Stroke Diagnosis System Using Electroencephalogram (EEG),” Biomedical Signal Processing and Control, vol. 87, no. 1, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Andrei M. Tudose et al., “Increasing Distributed Generation Hosting Capacity Based on a Sequential Optimization Approach Using an Improved Salp Swarm Algorithm,” Mathematics, vol. 12, no. 1, pp. 1-21, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Majdi Mafarja et al., “Classification Framework for Faulty-Software Using Enhanced Exploratory Whale Optimizer-Based Feature Selection Scheme and Random Forest Ensemble Learning,” Applied Intelligence, vol. 53, pp. 18715-18757, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[30] D. V. Manjunatha et al., “An Enhanced Video Compression Approach through RLAH Encoding and KDENN Algorithms,” EURASIP Journal on Advances in Signal Processing, vol. 2024, pp. 1-19, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Asmaa M. Khalid et al., “A New Binary Object-Oriented Programming Optimization Algorithm for Solving High-Dimensional Feature Selection Problem,” Alexandria Engineering Journal, vol. 85, pp.72-85, 2023.
[CrossRef] [Google Scholar] [Publisher Link]