Predicting early stage drug induced parkinsonism using unsupervised and supervised machine learning

Parvathy Nair, Roth Trisno, Maryam Shojaei Baghini, Gita Pendharkar, Hoam Chung

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

Abstract

Drug Induced Parkinsonism (DIP) is the most common, debilitating movement disorder induced by antipsychotics. There is no tool available in clinical practice to effectively diagnose the symptoms at the onset of the disease. In this study, the variations in gait accelerometer data due to the intermittency of tremor at the initial stages is examined. These variations are used to train a logistic regression model to predict subjects with early-stage DIP. The logistic classifier predicts if a subject is a DIP or control with approximately 89% sensitivity and 96% specificity. This paper discusses the algorithm used to extract the features in gait data for training the classifier to predict DIP at the earliest.Clinical Relevance - Diagnosing the disease and the causative drug is vital as the physical health of a patient who is mentally unstable can deteriorate with prolonged usage of the drug. The proposed model helps clinicians to diagnose the disease at the onset of tremors with an accuracy of 93.58%.

Original languageEnglish
Title of host publication2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages776-779
Number of pages4
ISBN (Electronic)9781728119908
DOIs
Publication statusPublished - 2020
EventInternational Conference of the IEEE Engineering in Medicine and Biology Society 2020 - Montreal, Canada
Duration: 20 Jul 202024 Jul 2020
Conference number: 42nd
https://embc.embs.org/2020/
https://ieeexplore.ieee.org/xpl/conhome/9167168/proceeding (Proceedings)

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2020-July
ISSN (Print)1557-170X

Conference

ConferenceInternational Conference of the IEEE Engineering in Medicine and Biology Society 2020
Abbreviated titleEMBC 2020
Country/TerritoryCanada
CityMontreal
Period20/07/2024/07/20
Internet address

Cite this