Εφαρμογές μηχανικής μάθησης στη νόσο του Parkinson
Applications of machine learning in Parkinson's disease
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Keywords
Νόσος Πάρκινσον ; Μηχανική μάθηση ; Στατιστική ; Holter ; Νευρωνικά δίκτυα ; Machine learning ; Parkinson's disease ; Neural networks ; StatisticsAbstract
Nowadays, machine learning has become an essential tool in the fight against Parkinson's
disease. With its ability to analyse vast amounts of data, machine learning can assist in the early
detection and accurate diagnosis of Parkinson's disease. It can also help track disease
progression and predict patient outcomes. Machine learning algorithms can also be used to
identify potential new drug targets and evaluate the efficacy of existing treatments. Moreover,
machine learning can aid in the development of personalized treatment plans for patients, taking
into account individual differences in disease presentation and response to therapies. Overall,
machine learning has the potential to revolutionize our understanding and treatment of
Parkinson's disease, ultimately improving patient outcomes and quality of life. In this thesis, an
extensive description is given of problems that are relevant to the treatment of Parkinson's
Disease, with more emphasis on Holter data management to make a decision, using machine
learning methods, about which patients are suitable for deep brain stimulation (DBS).
Postgraduate Studies Programme
Εφαρμοσμένη ΣτατιστικήDepartment
Σχολή Χρηματοοικονομικής και Στατιστικής. Τμήμα Στατιστικής και Ασφαλιστικής ΕπιστήμηςNumber of pages
113Language
GreekCollections
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Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα
Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα