The prevalence of depression and anxiety among Iranian people with multiple sclerosis: A systematic review and meta-analysis.

Shafiee, Arman; Soltani, Hedieh; Teymouri Athar, Mohammad Mobin; Jafarabady, Kyana; Mardi, Parham

doi:10.1016/j.msard.2023.104922

Cited by 2 publications

(1 citation statement)

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“…Traditional MS recognition methods have several shortcomings, which have led to the development and adoption of more advanced diagnostic techniques. Some of the main shortcomings [9] of traditional MS recognition methods include (i) Reliance on Clinical Symptoms: Traditional MS diagnosis often relies on clinical symptoms alone. Since MS symptoms can be nonspecific and mimic other conditions [10], this approach may lead to delayed or misdiagnosis, as many other diseases can produce similar symptoms [11].…”

Section: Introductionmentioning

confidence: 99%

Multiple Sclerosis Recognition via Wavelet Entropy and PSO-Based Neural Network

Lima

2023

Preprint

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This paper presents a novel approach for the recognition of multiple sclerosis (MS) using wavelet entropy and a Particle Swarm Optimization (PSO)-based neural network. MS is a complex neurological disorder with diverse clinical manifestations, often challenging to diagnose accurately. In this study, we leverage wavelet entropy (WE) as a feature extraction method to capture intricate patterns within brain imaging data. These extracted features are then employed to train a neural network model optimized through PSO to enhance classification accuracy. Experimental results on a dataset comprising MS and healthy control subjects demonstrate the effectiveness of our proposed approach, viz., WE-PSONN, which achieves a sensitivity of 91.95±1.15, a specificity of 92.36±0.88, a precision of 92.28±0.88, an accuracy of 92.16±0.90. The combination of advanced signal processing techniques and machine learning optimization holds promise for improving the early diagnosis and management of MS, offering potential benefits to both patients and healthcare providers.

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Section: Introductionmentioning

confidence: 99%