Multimodal Data Fusion and Machine Learning for Comprehensive Management of Parkinson's Disease in Healthcare

Parkinson's disease is a neurological ailment that affects millions of individuals throughout the world. Successful treatment will greatly enhance the quality of life for those who have it. To effectively treat Parkinson's disease, this study investigates how multimodal data and machine learning technology might be combined. To improve illness diagnosis, symptom severity prediction, and medication efficacy evaluation, the suggested technique draws from a broad variety of data sources such as clinical records, medical imaging, and sensor data. In this study, novel data fusion and analytic techniques are used to generate a comprehensive picture of the patient's health. Classification and prediction can be accomplished using either the Random Forest, Convolutional Neural Network, or Long Short-Term Memory Network machine learning techniques. Data fusion and feature extraction, the first steps in building reliable models, are graphically shown by a set of mathematical equations. Data presented in the tables of comparison show how much of an improvement the proposed remedy represents over the status quo. This strategy has the potential to significantly alter the way doctors treat Parkinson's disease, leading to better outcomes for patients and setting a new standard for healthcare in general.