Antioxidant and Anti-inflammatory Diagnostic Biomarkers in Multiple Sclerosis: A Machine Learning Study

Mezzaroba, Leda; Simão, Andréa Name Colado; Oliveira, Sayonara Rangel; Flauzino, Tamires; Alfieri, Daniela Frizon; Pereira, Wildéa Lice de Carvalho Jennings; Kallaur, Ana Paula; Lozovoy, Marcell Alysson Batisti; Kaimen-Maciel, Damacio Ramón; Maes, Michael; Reiche, Edna Maria Vissoci

doi:10.1007/s12035-019-01856-7

Cited by 36 publications

(23 citation statements)

References 55 publications

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“…Mezzaroba et al [ 29 ] aimed to evaluate indicators of MS disease in order to enable MS diagnosis. The study included 174 MS patients and 182 healthy controls.…”

Section: Related Studiesmentioning

confidence: 99%

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Aslam

Khan

Bashamakh

et al. 2022

Sensors

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Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients’ data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.

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“…Mezzaroba et al [ 29 ] aimed to evaluate indicators of MS disease in order to enable MS diagnosis. The study included 174 MS patients and 182 healthy controls.…”

Section: Related Studiesmentioning

confidence: 99%

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Aslam

Khan

Bashamakh

et al. 2022

Sensors

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“…10,11 Yakın zamanda yapılan bir çalışmada, antiinflamatuar besinler ve diyet takviyeleri ile beslenme müdahalesinin proinflamatuar bileşiklerin biyolojik sentezini azalttığı ve immünomodülatör ilaçları daha etkili hâle getirdiği, bunun da MS hastalarının klinik açıdan iyileştirdiği gözlenmiştir. 12 Bu derlemenin amacı, MS hastalığının klinik bulgularını incelemek, hastalığa yönelik bazı besin ögelerinin etkilerini analiz etmek ve hastalığa bağlı olarak görülen beslenme sorunlarına öneri sunmaktır.…”

Section: Multipl Skleroz Hastalığında Beslenme Ve Beslenme Sorunları:...unclassified

“…39,41 MS hastalığında artan oksidatif stresin normalizasyonu için antioksidan özellikte olan besin ögeleri oldukça önemli olup, optimal alım sağlandığında antioksidan enzim sekresyonu artarak, antioksidan savunma sisteminin normal işleyişine yardımcı olabilmektedir. 10,12,46 Bu sebeple vitamin-mineral ve biyoaktif bileşenlerin beslenme programında birey-sel gereksinime uygun miktarda bulunması gerekmektedir. 42,43 Ancak MS hastalığında etkinliği kanıtlanmış bir besin ögesi bulunmadığı için bu konuya yönelik klinik ve metaanaliz çalışmalarına gereksinim vardır.…”

Section: Diğer Mikro Besin öGeleriunclassified

Nutrition and Nutritional Problems in Multiple Sclerosis Disease: Traditional Review

ÖZMUTAF¹

2022

Turkiye Klinikleri J Health Sci

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Multipl skleroz (MS), santral sinir sistemini etkileyen kronik otoimmün bir hastalıktır. 1 MS ilk olarak 1868 yılında Jean-Martin Charcot tarafından tanımlanmıştır. 2 MS hastalığı genellikle ataklarla seyretmektedir ve atak sonrasında sekeller birikmekte-dir. Bu sebeple progresif dejeneratif bir süreç gelişerek hastaların yaşam kalitesi ciddi oranda azalmakta ve progresif engelliliğe neden olmaktadır. 2 Dünya çapında prevalansı yaklaşık 2,3 milyon iken, Türkiye İstatistik Kurumu 2017 verilerine göre sinir hastalık-

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“…MS could also be diagnosed with some biomarkers as shown in the work in [ 21 ]. The biomarkers suggested in [ 21 ] are the plasma levels of Tumor Necrosis Factor (TNF)-

, soluble TNF Receptor (sTNFR) 1, sTNFR2, adiponectin, hydroperoxides, Advanced Oxidation Protein Products (AOPPs), nitric oxide metabolites, total plasma antioxidant capacity using the Total Radical-trapping Antioxidant Parameter (TRAP), Sulfhydryl (SH) groups, and serum levels of zinc. Support vector machines were used on 174 MS patients and 182 controls and achieved a training accuracy of 92.9% and a validation accuracy of 90.6%.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Multiple Sclerosis Disease Diagnosis via an Ensemble Approach

Torkey

Belal

2022

Diagnostics

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Multiple Sclerosis (MS) is a disease attacking the central nervous system. According to MS Atlas’s most recent statistics, there are more than 2.8 million people worldwide diagnosed with MS. Recently, studies started to explore machine learning techniques to predict MS using various data. The objective of this paper is to develop an ensemble approach for diagnosis of MS using gene expression profiles, while handling the class imbalance problem associated with the data. A hierarchical ensemble approach employing voting and boosting techniques is proposed. This approach adopts a heterogeneous voting approach using two base learners, random forest and support vector machine. Experiments show that our approach outperforms state-of-the-art methods, with the highest recorded accuracy being 92.81% and 93.5% with BoostFS and DEGs for feature selection, respectively. Conclusively, the proposed approach is able to efficiently diagnose MS using the gene expression profiles that are more relevant to the disease. The approach is not merely an ensemble classifier outperforming previous work; it also identifies differentially expressed genes between normal samples and patients with multiple sclerosis using a genome-wide expression microarray. The results obtained show that the proposed approach is an efficient diagnostic tool for MS.

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Antioxidant and Anti-inflammatory Diagnostic Biomarkers in Multiple Sclerosis: A Machine Learning Study

Cited by 36 publications

References 55 publications

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Nutrition and Nutritional Problems in Multiple Sclerosis Disease: Traditional Review

An Enhanced Multiple Sclerosis Disease Diagnosis via an Ensemble Approach

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