AI Based Machine Learning Prediction Measure Parkinson Disease Severity

Parkinson’s disease is a neurodegenerative condition that affects millions of people worldwide. Enhancing patient care and treatment strategies requires accurate and efficient assessment of the disease’s severity. This research provides a new approach to artificial intelligence (AI)-based machine learning techniques for Parkinson’s disease severity prediction. A sizable dataset was acquired that included medical history, demographic information, and specific evaluations of the degree of Parkinson’s disease. To prepare it for training and testing machine learning models, the dataset underwent extensive feature selection, normalization, and data preprocessing. Many classification algorithms, including Support Vector Machines, Decision Trees, and Random Forests, were employed to build predictive models. The models were trained on a subset of the data, and hyperparameter optimization was utilized to enhance the models’ functionality. Evaluation metrics like area under the ROC curve, F1 score, accuracy, precision, and recall were used to assess the models’ predictive power.