Project ID: P202103100001
Drug Amount Prediction in Parkinson’s Disease Using Hybrid Machine Learning Systems and Radiomics Features
People with the below expertise are able to apply for this project: 1- The individual with enough experience in deep learning classification algorithms. 2- The individual with enough experience in traditional and deep learning fusion techniques. 3- The individual with enough experience in deep learning attention map. 4-Some learners who are familiar with images, image processing methods, and machine learning techniques. Both programming languages Matlab and Python are acceptable, but Python works better for some people who aim at working on google Colab.
Objectives: Parkinson’s Disease (PD) is progressive and heterogeneous. Predicting and personalizing drug amount consistently to treat PD patients holds significant promises to enhance chances of successful temporal symptomatic therapy. Here, we aim to predict the amount of Levodopa prescribed by physicians, using Hybrid-Machine-Learning-Systems (HMLS). Methods: We selected 264 patients from the PPMI. Seven datasets were generated with 950 features including clinical-features (CF) and radiomics-features (RFs) extracted from DAT-SPECT. We selected eight outcomes including patients being on/off drug, amount of dose in specific years, and increase in drug amount, in different years. For each outcome, we considered seven datasets normalized by Z-score techniques: i,ii,iii) data in year 0, 1, or 2; iv,v) longitudinal-datasets in years 0&1, and 0&1&2, vi,vii) timeless datasets in years 0&1, and years 0&1&2 (doubling&tripling data-samples respectively). HMLSs utilized included 14 feature-extraction (FEA) and 10 feature-selection-algorithms (FSA) followed by 25 regressors and 17 classifiers (optimized by 5-fold-cross-validation and Grid-search).