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Faghri, F., Hashemi, S.H., Leonard, H., Scholz, S.W., Campbell, R.H., Nalls, M.A., et al. (2018) Predicting Onset, Progression, and Clinical Subtypes of Parkinson Disease Using Machine Learning. bioRxiv. https://doi.org/10.1101/338913

has been cited by the following article:

• TITLE: Analysis, Identification and Prediction of Parkinson’s Disease Sub-Types and Progression through Machine Learning

  AUTHORS: Ashwin Ram

  KEYWORDS: Machine Learning, Neuroscience, Biocomputation, Parkinson’s Disease, Clustering, Dimensionality Reduction, Signal Processing, Artificial Intelligence

  JOURNAL NAME: Open Access Library Journal, Vol.11 No.1, January 31, 2024

  ABSTRACT: This paper represents a groundbreaking advancement in Parkinson’s disease (PD) research by employing a novel machine learning framework to categorize PD into distinct subtypes and predict its progression. Utilizing a comprehensive dataset encompassing both clinical and neurological parameters, the research applies advanced supervised and unsupervised learning techniques. This innovative approach enables the identification of subtle, yet critical, patterns in PD manifestation, which traditional methodologies often miss. Significantly, this research offers a path toward personalized treatment strategies, marking a major stride in the precision medicine domain and showcasing the transformative potential of integrating machine learning into medical research.