Mohammad R Salmanpour1, Mojtaba Shamsaei1, Abdollah Saberi2, Ivan S Klyuzhin3, Jing Tang4, Vesna Sossi5, Arman Rahmim6. 1. Department of Energy Engineering and Physics, Amirkabir University of Technology, Tehran, Iran. 2. Department of Computer Engineering, Islamic Azad University, Tehran, Iran. 3. Department of Medicine, University of British Columbia, Vancouver, BC, Canada. 4. Department of Electrical & Computer Engineering, Oakland University, Rochester, MI, USA. 5. Department of Physics & Astronomy, University of British Columbia, Vancouver, BC, Canada. 6. Department of Physics & Astronomy, University of British Columbia, Vancouver, BC, Canada; Department of Radiology, University of British Columbia, Vancouver, BC, Canada; Department of Radiology, Johns Hopkins University, Baltimore, MD, USA. Electronic address: arman.rahmim@ubc.ca.
Abstract
PURPOSE: It is vital to appropriately power clinical trials towards discovery of novel disease-modifying therapies for Parkinson's disease (PD). Thus, it is critical to improve prediction of outcome in PD patients. METHODS: We systematically probed a range of robust predictor algorithms, aiming to find best combinations of features for significantly improved prediction of motor outcome (MDS-UPDRS-III) in PD. We analyzed 204 PD patients with 18 features (clinical measures; dopamine-transporter (DAT) SPECT imaging measures), performing different randomized arrangements and utilizing data from 64%/6%/30% of patients in each arrangement for training/training validation/final testing. We pursued 3 approaches: i) 10 predictor algorithms (accompanied with automated machine learning hyperparameter tuning) were first applied on 32 experimentally created combinations of 18 features, ii) we utilized Feature Subset Selector Algorithms (FSSAs) for more systematic initial feature selection, and iii) considered all possible combinations between 18 features (262,143 states) to assess contributions of individual features. RESULTS: A specific set (set 18) applied to the LOLIMOT (Local Linear Model Trees) predictor machine resulted in the lowest absolute error 4.32 ± 0.19, when we firstly experimentally created 32 combinations of 18 features. Subsequently, 2 FSSAs (Genetic Algorithm (GA) and Ant Colony Optimization (ACO)) selecting 5 features, combined with LOLIMOT, reached an error of 4.15 ± 0.46. Our final analysis indicated that longitudinal motor measures (MDS-UPDRS-III years 0 and 1) were highly significant predictors of motor outcome. CONCLUSIONS: We demonstrate excellent prediction of motor outcome in PD patients by employing automated hyperparameter tuning and optimal utilization of FSSAs and predictor algorithms.
PURPOSE: It is vital to appropriately power clinical trials towards discovery of novel disease-modifying therapies for Parkinson's disease (PD). Thus, it is critical to improve prediction of outcome in PDpatients. METHODS: We systematically probed a range of robust predictor algorithms, aiming to find best combinations of features for significantly improved prediction of motor outcome (MDS-UPDRS-III) in PD. We analyzed 204 PDpatients with 18 features (clinical measures; dopamine-transporter (DAT) SPECT imaging measures), performing different randomized arrangements and utilizing data from 64%/6%/30% of patients in each arrangement for training/training validation/final testing. We pursued 3 approaches: i) 10 predictor algorithms (accompanied with automated machine learning hyperparameter tuning) were first applied on 32 experimentally created combinations of 18 features, ii) we utilized Feature Subset Selector Algorithms (FSSAs) for more systematic initial feature selection, and iii) considered all possible combinations between 18 features (262,143 states) to assess contributions of individual features. RESULTS: A specific set (set 18) applied to the LOLIMOT (Local Linear Model Trees) predictor machine resulted in the lowest absolute error 4.32 ± 0.19, when we firstly experimentally created 32 combinations of 18 features. Subsequently, 2 FSSAs (Genetic Algorithm (GA) and Ant Colony Optimization (ACO)) selecting 5 features, combined with LOLIMOT, reached an error of 4.15 ± 0.46. Our final analysis indicated that longitudinal motor measures (MDS-UPDRS-III years 0 and 1) were highly significant predictors of motor outcome. CONCLUSIONS: We demonstrate excellent prediction of motor outcome in PDpatients by employing automated hyperparameter tuning and optimal utilization of FSSAs and predictor algorithms.
Authors: Jennapher Lingo VanGilder; Cielita Lopez-Lennon; Serene S Paul; Leland E Dibble; Kevin Duff; Sydney Y Schaefer Journal: Front Rehabil Sci Date: 2021-10-22