Big Data Analytics for Predictive Modelling in Parkinson’s Disease
Volume 9 Issue 1 August 2023, pages 54-61
Received 13th July 2023; Accepted 17th July 2023
Parkinson’s Disease (PD) presents a complex landscape of heterogenous manifestations and individualized progression paths. Traditional research methodologies, while valuable, have limitations that restrict our understanding and management of this multifaceted disease. In this paper, we explore the transformative potential of big data analytics in PD research and care. By harnessing large, diverse, and dynamic datasets, big data analytics enables us to uncover intricate patterns, relationships, and predictors of disease progression that traditional methods may fail to reveal. We delve into the application of big data analytics in understanding the complex pathophysiology of PD, enhancing predictive modeling for disease progression, and facilitating the creation of personalized treatment plans. We further highlight the importance of addressing the significant challenges and ethical considerations associated with the use of big data. As we embark on this promising frontier in PD research, big data analytics presents an unprecedented opportunity to revolutionize our understanding of the disease and transform patient care.
Keywords:PARKINSON'S DISEASE, BIG DATA ANALYTICS, PREDICTIVE MODELLING, PERSONALIZED MEDICINE, DISEASE PROGRESSION, MACHINE LEARNING, WEARABLE TECHNOLOGY, GENETIC PROFILES, DATA INTEGRATION, ETHICAL CONSIDERATIONS
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