首页|University of Sheffield Reports Findings in Dementia (Ranking and filtering of neuropathology features in the machine learning evaluation of dementia studies)
University of Sheffield Reports Findings in Dementia (Ranking and filtering of neuropathology features in the machine learning evaluation of dementia studies)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Neurodegenerative Diseases and Conditions - Dementia is the subject of a report. According to news originating from Sheffield, United Kingdom, by NewsRx correspondents, research stated, “Early diagnosis of dementia diseases, such as Alzheimer’s disease, is difficult because of the time and resources needed to perform neuropsychological and pathological assessments. Given the increasing use of machine learning methods to evaluate neuropathology features in the brains of dementia patients, it is important to investigate how the selection of features may be impacted and which features are most important for the classification of dementia.” Our news journalists obtained a quote from the research from the University of Sheffield, “We objectively assessed neuropathology features using machine learning techniques for filtering features in two independent ageing cohorts, the Cognitive Function and Aging Studies (CFAS) and Alzheimer’s Disease Neuroimaging Initiative (ADNI). The reliefF and least loss methods were most consistent with their rankings between ADNI and CFAS; however, reliefF was most biassed by feature-feature correlations. Braak stage was consistently the highest ranked feature and its ranking was not correlated with other features, highlighting its unique importance. Using a smaller set of highly ranked features, rather than all features, can achieve a similar or better dementia classification performance in CFAS (60%-70% accuracy with Naive Bayes). This study showed that specific neuropathology features can be prioritised by feature filtering methods, but they are impacted by feature-feature correlations and their results can vary between cohort studies.”
SheffieldUnited KingdomEuropeBrain Diseases and Con- ditionsCentral Nervous System Diseases and ConditionsCyborgsDementiaEmerging TechnologiesHealth and MedicineMachine LearningMental HealthNeurodegenerative Diseases and ConditionsNeu- ropathology
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