Application of machine learning for early diagnosis of Parkinson’s disease

Amitava Datta, Sudip Paul

Research output: Chapter in Book/Conference paperChapter

Abstract

Parkinson’s disease is a slow and degenerative pathology of the central nervous system, which affects a person’s ability to control his movements. It affects between seven and ten million people around the globe. In US alone, 1.2 million people live with the disease, most over the age of 50. The disease develops gradually over time and the first signs are so imperceptible that they often go unnoticed. Although we know that some symptoms occur several years before the diagnosis of the disease, there is no specific way to detect the disease in the initial phase. MR imaging is often used to detect Parkinson’s. However, this method is very valid for selected cases with diagnostic difficulties. The cost is high. The patient has no significant side effects. It is not suitable for patients who have been diagnosed and treated. Therefore, scientists have come up with a new method for detecting Parkinson using machine learning
Original languageEnglish
Title of host publicationSmart Healthcare for Disease Diagnosis and Prevention
Place of PublicationLondon
PublisherElsevier
Chapter6
Pages33-41
ISBN (Print)9780128179130
DOIs
Publication statusPublished - 2020

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  • Cite this

    Datta, A., & Paul, S. (2020). Application of machine learning for early diagnosis of Parkinson’s disease. In Smart Healthcare for Disease Diagnosis and Prevention (pp. 33-41). Elsevier. https://doi.org/10.1016/B978-0-12-817913-0.00006-7