Selecting critical features for data classification based on machine learning methods

RC Chen, C Dewi, SW Huang, RE Caraka - Journal of Big Data, 2020 - Springer
Feature selection becomes prominent, especially in the data sets with many variables and
features. It will eliminate unimportant variables and improve the accuracy as well as the …

[HTML][HTML] Correlation between the gut microbiome and neurodegenerative diseases: a review of metagenomics evidence

X Liu, Y Liu, J Liu, H Zhang, C Shan… - Neural Regeneration …, 2024 - journals.lww.com
A growing body of evidence suggests that the gut microbiota contributes to the development
of neurodegenerative diseases via the microbiota-gut-brain axis. As a contributing factor …

From concept to practice: a scoping review of the application of AI to aphasia diagnosis and management

A Adikari, N Hernandez, D Alahakoon… - Disability and …, 2024 - Taylor & Francis
Purpose Aphasia is an acquired communication disability resulting from impairments in
language processing following brain injury, most commonly stroke. People with aphasia …

Reported adverse effects and attitudes among Arab populations following COVID-19 vaccination: a large-scale multinational study implementing machine learning …

MM Hatmal, MAI Al-Hatamleh, AN Olaimat… - Vaccines, 2022 - mdpi.com
Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19)
has imposed huge challenges on the healthcare facilities, and impacted every aspect of life …

Selecting features by utilizing intuitionistic fuzzy Entropy method

K Pandey, A Mishra, P Rani, J Ali… - … in Management and …, 2023 - dmame-journal.org
Feature selection is the most significant pre-processing activity, which intends to reduce the
data dimensionality for enhancing the machine learning process. The evaluation of feature …

R-HEFS: Rough set based heterogeneous ensemble feature selection method for medical data classification

RK Bania, A Halder - Artificial Intelligence in Medicine, 2021 - Elsevier
Feature selection is one of the trustworthy processes of dimensionality reduction technique
to select a subset of relevant and non-redundant features from large datasets. Ensemble …

[HTML][HTML] Automated bone age assessment using artificial intelligence: the future of bone age assessment

BD Lee, MS Lee - Korean journal of radiology, 2021 - ncbi.nlm.nih.gov
Bone age assessments are a complicated and lengthy process, which are prone to inter-and
intra-observer variabilities. Despite the great demand for fully automated systems …

The emerging role of long non-coding RNAs and microRNAs in neurodegenerative diseases: a perspective of machine learning

Á García-Fonseca, C Martin-Jimenez, GE Barreto… - Biomolecules, 2021 - mdpi.com
Neurodegenerative diseases (NDs) are characterized by progressive neuronal dysfunction
and death of brain cells population. As the early manifestations of NDs are similar, their …

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

JC Okeibunor, A Jaca, CJ Iwu-Jaja… - Frontiers in Public …, 2023 - frontiersin.org
Background Artificial intelligence (AI) is a broad outlet of computer science aimed at
constructing machines capable of simulating and performing tasks usually done by human …

Machine learning-based approach for efficient prediction of toxicity of chemical gases using feature selection

AM Erturan, G Karaduman, H Durmaz - Journal of hazardous materials, 2023 - Elsevier
Toxic gases can be fatal as they damage many living tissues, especially the nervous and
respiratory systems. They can cause permanent damage for many years by harming …