Project ID: P202102100003
Cognitive Outcome Prediction in Parkinson’s Disease Using Hybrid Machine Learning Systems and Radiomics Features
Machine learning, image processing
People with the below expertise are able to apply for this project: 1-The individual with enough experience in traditional and deep learning fusion techniques. 2-The individual with enough experience in extracting radiomics features from each region of interest (via SERA package). 3-The individual with enough experience in machine learning algorithms including dimension reduction algorithms, classifiers. Both programming languages Matlab and Python are acceptable, but Python works better for some people who aim at working on google Colab.
Objective: Montreal Cognitive Assessment (MoCA) as a rapid nonmotor-screening test assesses different aspects of cognitive dysfunction. Early prediction of these symptoms may facilitate better temporal therapy, disease control and identification of disease mechanisms. We aimed to predict MoCA scores in PD patients (year 4) from year 0 and 1 data. Methods: We collected 210 samples from the PPMI, and predicted their MoCA scores (range: 0-30) in year-4. In our study, 951 features including clinical features, conventional imaging features, and radiomics features were extracted from each dorsal striatum on DAT SPECT via our standardized SERA radiomics package. Four datasets (normalized by the z-score) were generated: use of the mentioned features in only (i) year-0, or (ii) year-1, iii) longitudinal-data (putting cross-sectional-datasets longitudinally next to each other), and iv) timeless-data (doubling dataset size, listing both cross-sectional-datasets separately). To predict MoCA, various Hybrid-Machine-Learning-Systems (HMLS) including 14 feature-extraction (FEA) or 10 features-selection algorithms (FSA) followed by 28 prediction algorithms (optimized by 5-fold-cross-validation and grid-search-technique) were applied on the datasets.