[HTML][HTML] 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 …
[HTML][HTML] Imperative role of machine learning algorithm for detection of Parkinson's disease: review, challenges and recommendations
Parkinson's disease (PD) is a neurodegenerative disease that affects the neural, behavioral,
and physiological systems of the brain. This disease is also known as tremor. The common …
and physiological systems of the brain. This disease is also known as tremor. The common …
[HTML][HTML] Modern methods of diagnostics and treatment of neurodegenerative diseases and depression
N Shusharina, D Yukhnenko, S Botman, V Sapunov… - Diagnostics, 2023 - mdpi.com
This paper discusses the promising areas of research into machine learning applications for
the prevention and correction of neurodegenerative and depressive disorders. These two …
the prevention and correction of neurodegenerative and depressive disorders. These two …
[HTML][HTML] An efficient machine learning approach for diagnosing parkinson's disease by utilizing voice features
Parkinson's disease (PD) is a neurodegenerative disease that impacts the neural,
physiological, and behavioral systems of the brain, in which mild variations in the initial …
physiological, and behavioral systems of the brain, in which mild variations in the initial …
[HTML][HTML] Analysis of Parkinson's disease using an imbalanced-speech dataset by employing decision tree ensemble methods
Parkinson's disease (PD) currently affects approximately 10 million people worldwide. The
detection of PD positive subjects is vital in terms of disease prognostics, diagnostics …
detection of PD positive subjects is vital in terms of disease prognostics, diagnostics …
[HTML][HTML] Diagnostic classification of Parkinson's disease based on non-motor manifestations and machine learning strategies
M Martinez-Eguiluz, O Arbelaitz, I Gurrutxaga… - Neural Computing and …, 2023 - Springer
Non-motor manifestations of Parkinson's disease (PD) appear early and have a significant
impact on the quality of life of patients, but few studies have evaluated their predictive …
impact on the quality of life of patients, but few studies have evaluated their predictive …
Selenium effects on oxidative stress-induced calcium signaling pathways in Parkinson's disease
Parkinson's disease (PD) is a neurological disorder in which oxidative stress and reactive
oxygen species productions are proposed to be involved in its pathogenesis. Despite …
oxygen species productions are proposed to be involved in its pathogenesis. Despite …
Artificial intelligence and neuroscience: An update on fascinating relationships
N Gopinath - Process Biochemistry, 2023 - Elsevier
Innovative technologies such as Artificial Intelligence (AI), deep learning, Machine learning
and optogenetics have been considered key components in the contribution to the …
and optogenetics have been considered key components in the contribution to the …
[HTML][HTML] A computerized analysis with machine learning techniques for the diagnosis of Parkinson's disease: past studies and future perspectives
According to the World Health Organization (WHO), Parkinson's disease (PD) is a
neurodegenerative disease of the brain that causes motor symptoms including slower …
neurodegenerative disease of the brain that causes motor symptoms including slower …
[HTML][HTML] A machine learning approach for early prediction of gestational diabetes mellitus using elemental contents in fingernails
YN Chan, P Wang, KH Chun, JTS Lum, H Wang… - Scientific Reports, 2023 - nature.com
The aim of this pilot study was to predict the risk of gestational diabetes mellitus (GDM) by
the elemental content in fingernails and urine with machine learning analysis. Sixty seven …
the elemental content in fingernails and urine with machine learning analysis. Sixty seven …