Please use this identifier to cite or link to this item: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/8993
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dc.contributor.authorKumar, Ashwani-
dc.contributor.authorSingh, Tiratha Raj-
dc.date.accessioned2023-01-06T04:50:27Z-
dc.date.available2023-01-06T04:50:27Z-
dc.date.issued2017-
dc.identifier.urihttp://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/8993-
dc.description.abstractAlzheimer’s disease (AD) is a progressive, incurable and terminal neurodegenerative disorder of the brain and is associated with mutations in amyloid precursor protein, presenilin 1, presenilin 2 or apolipoprotein E, but its underlying mechanisms are still not fully understood. Healthcare sector is generating a large amount of information corresponding to diagnosis, disease identification and treatment of an individual. Mining knowledge and providing scientific decision-making for the diagnosis and treatment of disease from the clinical dataset are therefore increasingly becoming necessary. The current study deals with the construction of classifiers that can be human readable as well as robust in performance for gene dataset of AD using a decision tree. Models of classification for different AD genes were generated according to Mini- Mental State Examination scores and all other vital parameters to achieve the identification of the expression level of different proteins of disorder that may possibly determine the involvement of genes in various AD pathogenesis pathways. The effectiveness of decision tree in AD diagnosis is determined by information gain with confidence value (0.96), specificity (92 %), sensitivity (98 %) and accuracy (77 %). Besides this functional gene classification using different parameters and enrichment analysis, our finding indicates that the measures of all the gene assess in single cohorts are sufficient to diagnose AD and will help in the prediction of important parameters for other relevant assessments.en_US
dc.language.isoenen_US
dc.publisherJaypee University of Information Technology, Solan, H.P.en_US
dc.subjectDementiaen_US
dc.subjectAlzheimer’s diseaseen_US
dc.subjectMini-Mental State Examinationen_US
dc.subjectClusteringen_US
dc.subjectValidationen_US
dc.titleA New Decision Tree to Solve the Puzzle of Alzheimer’s Disease Pathogenesis Through Standard Diagnosis Scoring Systemen_US
dc.typeArticleen_US
Appears in Collections:Journal Articles



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