Journal
SENSORS
Volume 22, Issue 2, Pages -Publisher
MDPI
DOI: 10.3390/s22020409
Keywords
biosensors; Parkinson's disease; wearable devices; machine learning
Funding
- European Union
- Greek national funds through the Competitiveness, Entrepreneurship and Innovation Operational Programme, under the Call Research-Create-Innovate, project title: BIODIANEA: Bio-chips for the diagnosis and treatment of neurodegenerative diseases, focusing [T1EDK-05029, 5030363]
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Parkinson's disease is a progressive neurodegenerative disorder associated with dysfunction of dopaminergic neurons in the brain. Early diagnosis of PD is challenging, but sensor-based platforms and machine learning techniques have the potential to improve diagnostic and prognostic capabilities. This work examines the current situation of sensor-based approaches in PD diagnosis and explores the use of machine learning models for personalized risk prediction.
Parkinson's disease (PD) is a progressive neurodegenerative disorder associated with dysfunction of dopaminergic neurons in the brain, lack of dopamine and the formation of abnormal Lewy body protein particles. PD is an idiopathic disease of the nervous system, characterized by motor and nonmotor manifestations without a discrete onset of symptoms until a substantial loss of neurons has already occurred, enabling early diagnosis very challenging. Sensor-based platforms have gained much attention in clinical practice screening various biological signals simultaneously and allowing researchers to quickly receive a huge number of biomarkers for diagnostic and prognostic purposes. The integration of machine learning into medical systems provides the potential for optimization of data collection, disease prediction through classification of symptoms and can strongly support data-driven clinical decisions. This work attempts to examine some of the facts and current situation of sensor-based approaches in PD diagnosis and discusses ensemble techniques using sensor-based data for developing machine learning models for personalized risk prediction. Additionally, a biosensing platform combined with clinical data processing and appropriate software is proposed in order to implement a complete diagnostic system for PD monitoring.
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