Optimization of Features for Classification of Parkinson's Disease from Vocal Dysphonia

Farzana, Walia; Hossain, Qua Zi Delwar

doi:10.1109/ecace.2019.8679126

Cited by 2 publications

(1 citation statement)

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“…This is along with a binary PD-score for prediction. Hence, features are extracted from a candidate patient based on the sustained vowel phonation or via running speech test [20]. Regarding the sustained vowel phonation, the persons are requested to enunciate the vowel letter /a/ for a time of fewer than 10 seconds using microphones and recording equipment in the laboratory.…”

Section: The Proposed Cloud-based Pd Diagnosis Systemmentioning

confidence: 99%

Cloud-Based Parkinson’s Disease Diagnosis Using Machine Learning

Nasser¹,

Mahmood²

2021

MMEP

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Parkinson’s disease (PD) harms the human brain's nervous system and can affect the patient's life. However, the diagnosis of PD diagnosis in the first stages can lead to early treatment and save costs. In this paper, a cloud-based machine learning diagnosing intelligent system is proposed for the PD with respect to patient voice. The proposed system is composed of two stages. In the first stage, two machine learning approaches, Random-Forest (RF) and Long-Short-Term-Memory (LSTM) are applied to generate a model that can be used for early treatment of PD. In this stage, a feature selection method is used to choose the minimum subset of the best features, which can be utilized later to generate the classification model. In the second stage, the best diagnosis model is deployed in cloud computing. In this stage, an Android application is also designed to provide the interface to the diagnosis model. The performance evaluation of the diagnosis model is conducted based on the F-score accuracy measurement. The result shows that the LTSM model has superior accuracy with 95% of the F-score compared with the RF model. Therefore, the LSTM model is selected for implementing a cloud-based PD diagnosing application using Python and Java.

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Section: The Proposed Cloud-based Pd Diagnosis Systemmentioning

confidence: 99%