Classification-Based Screening of Parkinson’s Disease Patients through Graph and Handwriting Signals

Fratello, Maria; Cordella, Fulvio; Albani, Giovanni; Veneziano, Giuseppe; Marano, Giuseppe; Paffi, Alessandra; Pallotti, Antonio

doi:10.3390/asec2021-11128

Cited by 9 publications

(1 citation statement)

References 23 publications

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“…Maria Fratello et al (2021) [9], carried out a study in which data was collected from 9 PD patients and 22 healthy people. The data was collected in collaboration with Casa di Cura Le Terrazze institute where every subject but one was right-handed.…”

Section: Related Workmentioning

confidence: 99%

Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection

et al. 2023

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Parkinson's disease (PD) is one of the chronic neurological diseases whose progression is slow and symptoms have similarities with other diseases. Early detection and diagnosis of PD is crucial to prescribe proper treatment for patient's productive and healthy lives. The disease's symptoms are characterized by tremors, muscle rigidity, slowness in movements, balancing along with other psychiatric symptoms. The dynamics of handwritten records served as one of the dominant mechanisms which support PD detection and assessment. Several machine learning methods have been investigated for the early detection of this disease. But, most of these handcrafted feature extraction techniques predominantly suffer from low performance accuracy issues. This cannot be tolerable for dealing with detection of such a chronic ailment. To this end, an efficient deep learning model is proposed which can assist to have early detection of Parkinson's disease. The significant contribution of the proposed model is to select the most optimum features which have the effect of getting the high performance accuracies. The feature optimization is done through genetic algorithm wherein K-Nearest Neighbour technique. The proposed novel model results into detection accuracy higher than 95%, precision of 98%, area under curve of 0.90 with a loss of 0.12 only. The performance of proposed model is compared with some state of the art machine learning and deep learning based PD detection approaches to demonstrate the better detection ability of our model.

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Section: Related Workmentioning

confidence: 99%

Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection

et al. 2023

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Grapho-Phonological Signals-Based Machine Learning Models Development for the Depressive Disorder

Laganaro,

Mazza,

Marano

et al. 2023

Lecture Notes in Electrical Engineering

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Classification-Based Screening of Depressive Disorder Patients Through Graph, Handwriting and Voice Signals

Laganaro,

Mazza,

Marano

et al. 2023

2023 International Workshop on Biomedical Applications, Technologies and Sensors (BATS)

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Classification-Based Screening of Parkinson’s Disease Patients through Graph and Handwriting Signals

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Cited by 9 publications

References 23 publications

Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection

Deep Transfer Learning Based Parkinson’s Disease Detection Using Optimized Feature Selection

Grapho-Phonological Signals-Based Machine Learning Models Development for the Depressive Disorder

Classification-Based Screening of Depressive Disorder Patients Through Graph, Handwriting and Voice Signals

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