Comparative Analysis of Machine Learning and Ensemble Learning Classifiers for Parkinson’s Disease Detection

Goyal, Palak; Rani, Rinkle; Singh, Karamjeet

doi:10.1109/ican56228.2022.10007376

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…It iteratively trains learners while adjusting the training instance weights. Higher weights are assigned to instances with higher prediction errors, prompting subsequent learners to focus on them [29].…”

Section: Model Constructionmentioning

confidence: 99%

Application of UAV-Borne Visible-Infared Pushbroom Imaging Hyperspectral for Rice Yield Estimation Using Feature Selection Regression Methods

Shen,

Yan,

Yang

et al. 2024

Sustainability

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Rice yield estimation is vital for enhancing food security, optimizing agricultural management, and promoting sustainable development. However, traditional satellite/aerial and ground-based/tower-based platforms face limitations in rice yield estimation, and few studies have explored the potential of UAV-borne hyperspectral remote sensing for this purpose. In this study, we employed a UAV-borne push-broom hyperspectral camera to acquire remote sensing data of rice fields during the filling stage, and the machine learning regression algorithms were applied to rice yield estimation. The research comprised three parts: hyperspectral data preprocessing, spectral feature extraction, and model construction. To begin, the preprocessing of hyperspectral data involved geometric distortion correction, relative radiometric calibration, and rice canopy mask construction. Challenges in geometric distortion correction were addressed by tracking linear features during flight and applying a single-line correction method. Additionally, the NIR reflectance threshold method was applied for rice canopy mask construction, which was subsequently utilized for average reflectance extraction. Then, spectral feature extraction was carried out to reduce multicollinearity in the hyperspectral data. Recursive feature elimination (RFE) was then employed to identify the optimal feature set for model performance. Finally, six machine learning regression models (SVR, RFR, AdaBoost, XGBoost, Ridge, and PLSR) were used for rice yield estimation, achieving significant results. PLSR showed the best R2 of 0.827 with selected features, while XGBoost had the best R2 of 0.827 with full features. In addition, the spatial distribution of absolute error in rice yield estimation was assessed. The results suggested that this UAV-borne imaging hyperspectral-based approach held great potential for crop yield estimation, not only for rice but also for other crops.

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Section: Model Constructionmentioning

confidence: 99%

Application of UAV-Borne Visible-Infared Pushbroom Imaging Hyperspectral for Rice Yield Estimation Using Feature Selection Regression Methods

Shen,

Yan,

Yang

et al. 2024

Sustainability

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show abstract

“…RF also demonstrated faster training times and superior predictive capabilities for PD. 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.…”

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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Comparative Analysis of Machine Learning and Ensemble Learning Classifiers for Parkinson’s Disease Detection

Cited by 2 publications

References 21 publications

Application of UAV-Borne Visible-Infared Pushbroom Imaging Hyperspectral for Rice Yield Estimation Using Feature Selection Regression Methods

Application of UAV-Borne Visible-Infared Pushbroom Imaging Hyperspectral for Rice Yield Estimation Using Feature Selection Regression Methods

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

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