Study on Degenerative Parkinson's Disease Using Various Machine Learning Algorithms

Bajaj, Madhvan; Rawat, Priyanshu; Sharma, Vikrant; Vats, Satvik; Yadav, Satya Prakash; Kukreja, Vinay

doi:10.1109/asiancon58793.2023.10270216

Cited by 1 publication

(1 citation statement)

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“…Goyal et al [14] explored various ML and Ensemble Learning (EL) classifiers for PD prediction, finding Random Forest to achieve 82.37% accuracy and Light Gradient Boosted Machine (LGBM) to achieve 85.90% accuracy. Bajaj et al [15] demonstrated high accuracy rates (over 95%) with Random Forest and XG-Boost models for early PD detection using data from the UCI Machine Learning Repository. Additionally, significant performance improvements were observed for ResNet18 and a proposed Fully Connected Neural Network compared to existing models, as reported in recent research.…”

Section: Comparison With Recent Publicationsmentioning

confidence: 99%

Deep Learning based prediction and monitoring of Parkinson’s Disease using Voice Data

Maiti,

Sen,

Manna

et al. 2024

Preprint

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Accurately detecting Parkinson’s disease (PD) at an early stage is certainly indispensable for slowing down its progress and providing patients the possibility of accessing to disease-modifying therapy. An innovative deep-learning technique is proposed to predict and monitor the Parkinson’s Disease (PD) through voice data and Unified Parkinson’s Disease Rating Scale (UPDRS) score. The rise of an aging population over the world emphasizes the need to detect PD early, remotely and accurately. PD is a progressive neurological disorder that affects the Central Nervous System (CNS), leading to symptoms like tremors, stiffness, slow movements, balance and coordination difficulties, and speech disorders. Early detection and severity assessment of PD using Machine Learning is crucial. Speech recognition offers a new approach for diagnosis and monitoring of PD. This paper highlights Deep Learning models with acoustic features like jitter, shimmer, intensity and pitch for automatic detection and use of UPDRS score for severity assessment. Two datasets, one of them containing speech samples from PD patients and healthy individuals and another containing voice data and UPDRS score, with on disk size of 5.3 MB and 1.01 MB respectively, are used in this proposed work. A comparison between the proposed Residual Neural Network (ResNet) and other ten machine learning and ensemble learning methods based on relatively small data including 64 healthy individuals and 188 early PD patients shows the superior detection performance of the designed model, which achieves the highest accuracy, 98 % on average and precision, 0.98 and F1-Score, 0.98.

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Section: Comparison With Recent Publicationsmentioning

confidence: 99%