Binary grey wolf optimizer with mutation and adaptive k-nearest neighbour for feature selection in Parkinson's disease diagnosis
RR Rajammal, S Mirjalili, G Ekambaram… - Knowledge-Based …, 2022 - Elsevier
Disease identification and classification relies on Feature Selection (FS) to find the relevant
features for accurate medical diagnosis. FS is an optimization problem solved with the help …
features for accurate medical diagnosis. FS is an optimization problem solved with the help …
Diagnosis of Parkinson's disease using modified grey wolf optimization
This paper presents the Modified Grey Wolf Optimization (MGWO) algorithm which helps
with the identification of the symptoms of Parkinson's disease at a premature stage …
with the identification of the symptoms of Parkinson's disease at a premature stage …
A hybrid feature selection algorithm based on a discrete artificial bee colony for Parkinson's diagnosis
Parkinson's disease is a neurodegenerative disease that affects millions of people around
the world and cannot be cured fundamentally. Automatic identification of early Parkinson's …
the world and cannot be cured fundamentally. Automatic identification of early Parkinson's …
[HTML][HTML] Bayesian optimization with support vector machine model for parkinson disease classification
Parkinson's disease (PD) has become widespread these days all over the world. PD affects
the nervous system of the human and also affects a lot of human body parts that are …
the nervous system of the human and also affects a lot of human body parts that are …
Improved diagnosis of Parkinson's disease using optimized crow search algorithm
Diagnosis of Parkinson's disease at its early stage is important in proper treatment of the
patients so they can lead productive lives for as long as possible. Although many techniques …
patients so they can lead productive lives for as long as possible. Although many techniques …
[HTML][HTML] Memory-based sand cat swarm optimization for feature selection in medical diagnosis
The rapid expansion of medical data poses numerous challenges for Machine Learning
(ML) tasks due to their potential to include excessive noisy, irrelevant, and redundant …
(ML) tasks due to their potential to include excessive noisy, irrelevant, and redundant …
A random walk Grey wolf optimizer based on dispersion factor for feature selection on chronic disease prediction
K Deep - Expert Systems with Applications, 2022 - Elsevier
In the field of Chronic disease prediction, identifying the relevant features plays an important
role for early disease diagnosis. With a high dimensionality of data, search for an adequate …
role for early disease diagnosis. With a high dimensionality of data, search for an adequate …
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 …
Binary optimization using hybrid grey wolf optimization for feature selection
A binary version of the hybrid grey wolf optimization (GWO) and particle swarm optimization
(PSO) is proposed to solve feature selection problems in this paper. The original PSOGWO …
(PSO) is proposed to solve feature selection problems in this paper. The original PSOGWO …
A multi-agent feature selection and hybrid classification model for Parkinson's disease diagnosis
MA Mohammed, M Elhoseny… - ACM Transactions on …, 2021 - dl.acm.org
Parkinson's disease (PD) diagnostics includes numerous analyses related to the
neurological, physical, and psychical status of the patient. Medical teams analyze multiple …
neurological, physical, and psychical status of the patient. Medical teams analyze multiple …