A comprehensive identification-evidence based alternative for HIV/AIDS treatment with HAART in the healthcare industries. Issue 131 (July 2016)

• Record Type: Journal Article
• Authors: Chen, You-Shyang
• Is Part Of: Computer methods and programs in biomedicine. Issue 131(2016)
• Page Start: 111
• Page End: 126
• Publication Date: 2016-07
• Subjects: Human immunodeficiency virus (HIV) -- Acquired immune deficiency syndrome (AIDS) -- Highly active anti-retroviral therapy (HAART) -- Linear–nonlinear feature selection -- Hybrid model; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.04.001
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: To develop an integrated linear–nonlinear feature selection technique to identify the determinants of sustained HAART. To generate a hybrid model based on rough set classifiers to verify its performance. To create a relevant decision rule set of an LEM2 algorithm for specialist physicians as a diagnosis reference. To effectively offer the study findings and results from the given data set to relevant medical institutions and patients. Abstract: Background and Objective: The HIV/AIDS-related issue has given rise to a priority concern in which potential new therapies are increasingly highlighted to lessen the negative impact of highly active anti-retroviral therapy (HAART) in the healthcare industry. With the motivation of "medical applications, " this study focuses on the main advanced feature selection techniques and classification approaches that reflect a new architecture, and a trial to build a hybrid model for interested parties. Methods: This study first uses an integrated linear–nonlinear feature selection technique to identify the determinants influencing HAART medication and utilizes organizations of different condition-attributes to generate a hybrid model based on a rough set classifier to study evolving HIV/AIDS research in order to improve classification performance. Results: The proposed model makes use of a real data set from Taiwan's specialist medical center. The experimental results show that the proposed model yields a satisfactory result that is superior to the listed methods, and the core condition-attributes PVL, CD4, Code, Age, Year, PLT, and Sex were identified in the HIV/AIDS data set. In addition, the decision rule set created can be referenced as a knowledge-based healthcare service system as the best of evidence-based practices in the workflow of current clinical diagnosis. Conclusions: This study highlights the importance of these key factors and provides the rationale that the proposed model is an effective alternative to analyzing sustained HAART medication in follow-up studies of HIV/AIDS treatment in practice.