Parkinson's Disease Progression Prediction Using Longitudinal Imaging Data and Grey Wolf Optimizer-Based Feature Selection
Keywords:
Grey Wolf Optimizer, Imaging data, Progression Prediction, Parkinson Disease, Feature selectionAbstract
This work uses longitudinal imaging data and a feature selection method based on the Grey Wolf Optimizer (GWO) to create a novel method for forecasting the course of Parkinson's disease.Magnetic resonance imaging (MRI) and positron emission tomography (PET) longitudinal imaging data offer important insights into the structural and functional changes in the brain over time. However, because of its great dimensionality, analysing this complicated data might be difficult. We suggest using the GWO-based feature selection method to identify the most informative imaging features related to illness development in order to solve this problem.The Grey Wolf Optimizer is an algorithm that draws inspiration from nature and imitates the way that grey wolves hunt. By effectively locating an ideal subset of features that maximise classification or regression performance, it has demonstrated promising results in feature selection challenges. GWO will be used in our investigation to choose the most pertinent imaging features from the longitudinal data, lowering dimensionality and improving the model's ability to predict outcomes.Using machine learning strategies, we will build a predictive model that includes the chosen features and longitudinal imaging data. We hope to equip clinicians with a tool to forecast the course of each patient's Parkinson's disease by utilising this model. By assisting in early diagnosis, treatment planning, and disease progression monitoring, this predictive skill can ultimately improve the overall management of Parkinson's disease and the quality of life for those who are affected. Our method has great promise for expanding the fields of neurodegenerative disease prediction and personalised therapy because it integrates longitudinal imaging data and the Grey Wolf Optimizer-based feature selection method in a novel way.
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