Personalized Predictive Modeling for Rehabilitation Outcomes in Neurological Conditions through Machine Learning

Abstract

The intersection of machine learning and neurological rehabilitation is marked by a paradigm shift toward personalized healthcare. The objectives include developing robust machine learning models, individualizing rehabilitation plans based on model predictions, and evaluating the clinical and economic impact of personalized predictive modelling in neurological rehabilitation. The proposed data flow diagram outlines the pro-cess from external data sources to real-time monitoring, emphasizing data preprocessing, feature extraction, machine learning model development, and validation. The algorithm details the steps involved, incorporating K-Nearest Neighbours imputation and ensemble methods. Change Data Capture, Complementary Filter, and the pseudocode for predictive modelling are presented. Real-time monitoring with sensor fusion algorithms is explored. Results from a dataset of 1,047 patients demonstrate the model's ability to predict rehabilitation outcomes. Performance metrics, including precision, recall, and prediction error, highlight the model's accuracy and effectiveness. While some instances exhibit higher prediction errors, the overall robustness suggests promising implications for personalized rehabilitation. The study represents a significant advancement in personalized healthcare for neurological rehabilitation. Integrating machine learning in-to rehabilitation practices holds the potential to revolutionize patient care, providing tailored interventions for optimal outcomes. The outcomes show-case a transformative potential where interventions are not only effective but precisely tailored to individual neurological recovery journey.