Machine learning methods for optimal prediction of motor outcome in Parkinson’s disease
Mohammad R. Salmanpour(University of British Columbia), Abdollah Saberi(Islamic Azad University, Tehran), Ivan S. Klyuzhin(University of British Columbia), Vesna Sossi(University of British Columbia), Mojtaba Shamsaei(Amirkabir University of Technology), Jing Tang(Oakland University)
Cited by 55
Related Papers
The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights
|Radiology|2024|210
Prediction of Cognitive Decline in Parkinson’s Disease Using Clinical and DAT SPECT Imaging Features, and Hybrid Machine Learning Systems
|Diagnostics|2023|88
Deep versus Handcrafted Tensor Radiomics Features: Prediction of Survival in Head and Neck Cancer Using Machine Learning and Fusion Techniques
|Diagnostics|2023|83
Fusion-based tensor radiomics using reproducible features: Application to survival prediction in head and neck cancer
|Computer Methods and Programs in Biomedicine|2023|78
Optimized machine learning methods for prediction of cognitive outcome in Parkinson's disease
|Computers in Biology and Medicine|2019|60