A Machine-learning Approach For The Early Diagnosis Of Parkinson’s Disease

In a recent study, scientists from the University of New South Wales (UNSW) discussed a machine learning (ML)-based tool that can detect Parkinson’s Disease (PD) years before the first onset of symptoms. At present, the overall diagnostic accuracy for PD based on motor symptoms is 80%. This accuracy could be increased if PD was diagnosed based on biomarkers rather than primarily depending on physical symptoms. Several diseases are detected based on biomarkers associated with metabolic processes, and non-invasive diagnostic methods using skin sebum and breath have recently gained popularity. ML approaches are widely used to develop accurate prediction models for disease diagnosis using large metabolomics data.