title: Data Mining in HIV-AIDS Surveillance System : Application to Portuguese Data.
abstract: The Human Immunodeficiency Virus (HIV) is an infectious agent that attacks the immune system cells. Without a strong immune system, the body becomes very susceptible to serious life threatening opportunistic diseases. In spite of the great progresses on medication and prevention over the last years, HIV infection continues to be a major global public health issue, having claimed more than 36 million lives over the last 35 years since the recognition of the disease. Monitoring, through registries, of HIV-AIDS cases is vital to assess general health care needs and to support long-term health-policy control planning. Surveillance systems are therefore established in almost all developed countries. Typically, this is a complex system depending on several stakeholders, such as health care providers, the general population and laboratories, which challenges an efficient and effective reporting of diagnosed cases. One issue that often arises is the administrative delay in reports of diagnosed cases. This paper aims to identify the main factors influencing reporting delays of HIV-AIDS cases within the portuguese surveillance system. The used methodologies included multilayer artificial neural networks (MLP), naive bayesian classifiers (NB), support vector machines (SVM) and the k-nearest neighbor algorithm (KNN). The highest classification accuracy, precision and recall were obtained for MLP and the results suggested homogeneous administrative and clinical practices within the reporting process. Guidelines for reductions of the delays should therefore be developed nationwise and transversally to all stakeholders.
authors.affiliation: Center of Mathematics, University of Porto, Porto, Portugal. aao@ess.ipp.pt.|Artificial Intelligence and Computer Science Laboratory, LIACC, Porto, Portugal. aao@ess.ipp.pt.|ESS-IPP - Higher School of Health, Polytechnic of Porto, Porto, Portugal. aao@ess.ipp.pt.
authors.forename: Alexandra
authors.initials: A
authors.lastname: Oliveira
authors.affiliation: Artificial Intelligence and Computer Science Laboratory, LIACC, Porto, Portugal.|ESS-IPP - Higher School of Health, Polytechnic of Porto, Porto, Portugal.
authors.forename: Brígida Mónica
authors.initials: BM
authors.lastname: Faria
authors.affiliation: Center of Mathematics, University of Porto, Porto, Portugal.|Department of Mathematics, Faculty of Sciences, University of Porto, Porto, Portugal.
authors.forename: A Rita
authors.initials: AR
authors.lastname: Gaio
authors.affiliation: Artificial Intelligence and Computer Science Laboratory, LIACC, Porto, Portugal.|DSI-EEUM - Information Systems Department, School of Engineering, University of Minho, Braga, Portugal.
authors.forename: Luís Paulo
authors.initials: LP
authors.lastname: Reis
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000175:diagnosis / Q000453:epidemiology; D000465:Algorithms; D001499:Bayes Theorem; D057225:Data Mining / Q000379:methods*; D015658:HIV Infections / Q000175:diagnosis* / Q000453:epidemiology*; D006801:Humans; D016571:Neural Networks, Computer; D011174:Portugal / Q000453:epidemiology; D060388:Support Vector Machine
keywords: Data mining; HIV-AIDS; Reporting delay; Surveillance data; Surveillance system