title: A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳s disease and mild cognitive impairment.
abstract: Population aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis.
authors.affiliation: Institute of Computing, Federal Fluminense University, Rua Passo da Pátria, 156, Niterói, RJ 24210-240, Brazil. Electronic address: fseixas@ic.uff.br.
authors.forename: Flávio Luiz
authors.initials: FL
authors.lastname: Seixas
authors.affiliation: IBM Research Brazil, Av. Pasteur, 138, Rio de Janeiro, RJ 22296-903, Brazil. Electronic address: biancaz@br.ibm.com.
authors.forename: Bianca
authors.initials: B
authors.lastname: Zadrozny
authors.affiliation: Center for Alzheimer׳s Disease and Related Disorder, Institute of Psychiatry, Federal University of Rio de Janeiro, Av. Venceslau Brás, 71, Rio de Janeiro, RJ 22290-140, Brazil. Electronic address: jlaks@centroin.com.br.
authors.forename: Jerson
authors.initials: J
authors.lastname: Laks
authors.affiliation: Institute of Computing, Federal Fluminense University, Rua Passo da Pátria, 156, Niterói, RJ 24210-240, Brazil. Electronic address: aconci@ic.uff.br.
authors.forename: Aura
authors.initials: A
authors.lastname: Conci
authors.affiliation: Institute of Computing, Federal Fluminense University, Rua Passo da Pátria, 156, Niterói, RJ 24210-240, Brazil. Electronic address: debora@ic.uff.br.
authors.forename: Débora Christina
authors.initials: DC
authors.lastname: Muchaluat Saade
mesh_terms: D000544:Alzheimer Disease / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology*; D001499:Bayes Theorem; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D001523:Mental Disorders / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology*; D008954:Models, Biological
keywords: Alzheimer׳s disease; Bayesian network; Clinical decision support system; Dementia; Mild cognitive impairment