detalle del documento
IDENTIFICACIÓN

oai:arXiv.org:2005.14257

Tema
Electrical Engineering and Systems... Computer Science - Machine Learnin... Computer Science - Sound Quantitative Biology - Quantitativ...
Autor
Iman, Mohammadreza Giuntini, Amy Arabnia, Hamid Reza Rasheed, Khaled
Categoría

Computer Science

Año

2020

fecha de cotización

1/6/2022

Palabras clave
science techniques parkinson methods disease voice using
Métrico

Resumen

People with Parkinson's disease must be regularly monitored by their physician to observe how the disease is progressing and potentially adjust treatment plans to mitigate the symptoms.

Monitoring the progression of the disease through a voice recording captured by the patient at their own home can make the process faster and less stressful.

Using a dataset of voice recordings of 42 people with early-stage Parkinson's disease over a time span of 6 months, we applied multiple machine learning techniques to find a correlation between the voice recording and the patient's motor UPDRS score.

We approached this problem using a multitude of both regression and classification techniques.

Much of this paper is dedicated to mapping the voice data to motor UPDRS scores using regression techniques in order to obtain a more precise value for unknown instances.

Through this comparative study of variant machine learning methods, we realized some old machine learning methods like trees outperform cutting edge deep learning models on numerous tabular datasets.

;Comment: Accepted at "HIMS'20 - The 6th Int'l Conf on Health Informatics and Medical Systems"; https://americancse.org/events/csce2020/conferences/hims20

Iman, Mohammadreza,Giuntini, Amy,Arabnia, Hamid Reza,Rasheed, Khaled, 2020, A Comparative Study of Machine Learning Models for Tabular Data Through Challenge of Monitoring Parkinson's Disease Progression Using Voice Recordings

Documento

Abrir

Compartir

Fuente

Artículos recomendados por ES/IODE IA

Investigation of Heavy Metal Analysis on Medicinal Plants Used for the Treatment of Skin Cancer by Traditional Practitioners in Pretoria
heavy metals medicinal plants skin cancer icp-ms health risk assessment treatment cancer plants 0 metal health medicinal