Cargando…

Prediction of Multiple sclerosis disease using machine learning classifiers: a comparative study

INTRODUCTION: Hamedan Province is one of Iran’s high-risk regions for Multiple Sclerosis (MS). Early diagnosis of MS based on an accurate system can control the disease. The aim of this study was to compare the performance of four machine learning techniques with traditional methods for predicting M...

Descripción completa

Detalles Bibliográficos
Autores principales:	DARVISHI, SONIA, HAMIDI, OMID, POOROLAJAL, JALAL
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Pacini Editore Srl 2021
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8283630/ https://www.ncbi.nlm.nih.gov/pubmed/34322636 http://dx.doi.org/10.15167/2421-4248/jpmh2021.62.1.1651

Ejemplares similares

Prediction of Kidney Graft Rejection Using Artificial Neural Network
por: Tapak, Leili, et al.
Publicado: (2017)

Real-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran
por: Tapak, Lily, et al.
Publicado: (2013)

Landmark Prediction of Survival for Breast Cancer Patients: A Case Study in Tehran, Iran
por: ALAFCHI, Behnaz, et al.
Publicado: (2019)

Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests
por: HAMIDI, Omid, et al.
Publicado: (2016)

Prediction of disease progression and outcomes in multiple sclerosis with machine learning
por: Pinto, Mauro F., et al.
Publicado: (2020)

Smoking and Suicide: A Meta-Analysis
por: Poorolajal, Jalal, et al.
Publicado: (2016)

Risk prediction of cardiovascular disease using machine learning classifiers
por: Pal, Madhumita, et al.
Publicado: (2022)

Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis
por: Tommasin, Silvia, et al.
Publicado: (2021)

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study
por: Visweswaran, Shyam, et al.
Publicado: (2020)

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers
por: Hassan, Ch. Anwar ul, et al.
Publicado: (2022)

Prediction of diabetes disease using an ensemble of machine learning multi-classifier models
por: Abnoosian, Karlo, et al.
Publicado: (2023)

Exploration of machine learning techniques in predicting multiple sclerosis disease course
por: Zhao, Yijun, et al.
Publicado: (2017)

The Prevalence of Psychiatric Distress and Associated Risk Factors among College Students Using GHQ-28 Questionnaire
por: POOROLAJAL, Jalal, et al.
Publicado: (2017)

Computational classifiers for predicting the short-term course of Multiple sclerosis
por: Bejarano, Bartolome, et al.
Publicado: (2011)

Alcohol-Related Risk of Suicidal Ideation, Suicide Attempt, and Completed Suicide: A Meta-Analysis
por: Darvishi, Nahid, et al.
Publicado: (2015)

Correction: Alcohol-Related Risk of Suicidal Ideation, Suicide Attempt, and Completed Suicide: A Meta-Analysis
por: Darvishi, Nahid, et al.
Publicado: (2020)

Comparing Machine Learning Classifiers for Predicting Hospital Readmission of Heart Failure Patients in Rwanda
por: Rizinde, Theogene, et al.
Publicado: (2023)

Relationship of Religion with Suicidal Ideation, Suicide Plan, Suicide Attempt, and Suicide Death: A Meta-analysis
por: Poorolajal, Jalal, et al.
Publicado: (2021)

The role of problem-solving skills in the prevention of suicidal behaviors: A systematic review and meta-analysis
por: Darvishi, Nahid, et al.
Publicado: (2023)

Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers
por: Mondal, Pritom Kumar, et al.
Publicado: (2023)

Machine Learning Use for Prognostic Purposes in Multiple Sclerosis
por: Seccia, Ruggiero, et al.
Publicado: (2021)

Multiple Sclerosis Associated Risk Factors: A Case-Control Study
por: POOROLAJAL, Jalal, et al.
Publicado: (2015)

A comparative analysis of machine learning classifiers for predicting protein-binding nucleotides in RNA sequences
por: Agarwal, Ankita, et al.
Publicado: (2022)

Prediction of RNA- and DNA-Binding Proteins Using Various Machine Learning Classifiers
por: Poursheikhali Asghari, Mehdi, et al.
Publicado: (2019)

Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers
por: Gómez-Pulido, Juan A., et al.
Publicado: (2021)

Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers
por: Song, Dixiang, et al.
Publicado: (2021)

Prediction of plant lncRNA by ensemble machine learning classifiers
por: Simopoulos, Caitlin M. A., et al.
Publicado: (2018)

Machine Learning-based Classifiers for the Prediction of Low Birth Weight
por: Arayeshgari, Mahya, et al.
Publicado: (2023)

Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features
por: Ion-Mărgineanu, Adrian, et al.
Publicado: (2017)

An Interpretable Machine Learning Model to Predict Cortical Atrophy in Multiple Sclerosis
por: Conti, Allegra, et al.
Publicado: (2023)

Vaccination frequency in people newly diagnosed with multiple sclerosis
por: Darvishi, Sonia, et al.
Publicado: (2023)

Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning
por: Sahli-Costabal, Francisco, et al.
Publicado: (2020)

Classifying grains using behaviour-informed machine learning
por: Laudari, Sudip, et al.
Publicado: (2022)

Predicting lung adenocarcinoma disease progression using methylation-correlated blocks and ensemble machine learning classifiers
por: Yu, Xin, et al.
Publicado: (2021)

gMS-Classifier1 does not predict disability progression in multiple sclerosis
por: van Rossum, Johannis A, et al.
Publicado: (2018)

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
por: Romagnoni, Alberto, et al.
Publicado: (2019)

Predicting disease activity in patients with multiple sclerosis: An explainable machine‐learning approach in the Mavenclad trials
por: Basu, Sreetama, et al.
Publicado: (2022)

A Machine Learning Classifier for Predicting Stable MCI Patients Using Gene Biomarkers
por: Lin, Run-Hsin, et al.
Publicado: (2022)

Predicting pre-service teachers’ computational thinking skills using machine learning classifiers
por: Jin, Hao-Yue, et al.
Publicado: (2023)

On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders
por: Ospina, Raydonal, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_s7cqd0giguhd9f8s4oresk28gj