Machine learning for the diagnosis of Parkinson's disease: a review of literature
Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and
assessment of clinical signs, including the characterization of a variety of motor symptoms …
assessment of clinical signs, including the characterization of a variety of motor symptoms …
Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms
AS Chandrabhatla, IJ Pomeraniec… - NPJ digital …, 2022 - nature.com
Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor
impairments such as tremor, bradykinesia, dyskinesia, and gait abnormalities. Current …
impairments such as tremor, bradykinesia, dyskinesia, and gait abnormalities. Current …
[PDF][PDF] RETRACTED ARTICLE: An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network
The objective of this study is to propose an alternative, hybrid solution method for
diagnosing diabetic retinopathy from retinal fundus images. In detail, the hybrid method is …
diagnosing diabetic retinopathy from retinal fundus images. In detail, the hybrid method is …
Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques
Neurodegenerative diseases have shown an increasing incidence in the older population in
recent years. A significant amount of research has been conducted to characterize these …
recent years. A significant amount of research has been conducted to characterize these …
Detecting Parkinson's disease with sustained phonation and speech signals using machine learning techniques
JS Almeida, PP Rebouças Filho, T Carneiro… - Pattern Recognition …, 2019 - Elsevier
This study investigates the processing of voice signals for detecting Parkinson's disease.
This disease is one of the neurological disorders that affect people in the world most. The …
This disease is one of the neurological disorders that affect people in the world most. The …
Prevalence and diagnosis of neurological disorders using different deep learning techniques: a meta-analysis
This paper dispenses an exhaustive review on deep learning techniques used in the
prognosis of eight different neuropsychiatric and neurological disorders such as stroke …
prognosis of eight different neuropsychiatric and neurological disorders such as stroke …
Optimized cuttlefish algorithm for diagnosis of Parkinson's disease
This paper presents an optimized cuttlefish algorithm for feature selection based on the
traditional cuttlefish algorithm, which can be used for diagnosis of Parkinson's disease at its …
traditional cuttlefish algorithm, which can be used for diagnosis of Parkinson's disease at its …
Application of deep learning models for automated identification of Parkinson's disease: A review (2011–2021)
Parkinson's disease (PD) is the second most common neurodegenerative disorder affecting
over 6 million people globally. Although there are symptomatic treatments that can increase …
over 6 million people globally. Although there are symptomatic treatments that can increase …
Handwriting dynamics assessment using deep neural network for early identification of Parkinson's disease
The etiology of Parkinson's disease (PD) remains unclear. Symptoms usually appear after
approximately 70% of dopamine-producing cells have stopped working normally. PD cannot …
approximately 70% of dopamine-producing cells have stopped working normally. PD cannot …
Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson's disease
An accurate diagnosis of Parkinson's disease by specialists involves many neurological,
psychological and physical examinations. The specialists investigate a number of symptoms …
psychological and physical examinations. The specialists investigate a number of symptoms …