AIMS AND OBJECTIVES: In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life.
CONCLUSION: The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.
METHODS AND ANALYSIS: This review will be conducted in accordance with the preferred reporting items for systematic review and meta-analyses protocols. Primary outcomes will include: (1) proportion of eligible patients initiating antiretroviral therapy (ART); (2) proportion of those on ART with <1000 copies/mL; (3) rate of all-cause mortality among ART recipients. Secondary outcomes will include: (1) proportion receiving Pneumocystis jiroveci pneumonia prophylaxis; (2) proportion with >90% ART adherence (based on any measure reported); (3) proportion screened for non-communicable diseases (specifically cervical cancer, diabetes, hypertension and mental ill health); (iv) proportion screened for tuberculosis. A search of five electronic bibliographical databases (Embase, Medline, PsychINFO, Web of Science and CINAHL) and reference lists of included articles will be conducted to identify relevant articles reporting HIV clinical outcomes. Searches will be limited to LMIC. No age, publication date, study-design or language limits will be applied. Authors of relevant studies will be contacted for clarification. Two reviewers will independently screen citations and abstracts, identify full text articles for inclusion, extract data and appraise the quality and bias of included studies. Outcome data will be pooled to generate aggregative proportions of primary and secondary outcomes. Descriptive statistics and a narrative synthesis will be presented. Heterogeneity and sensitivity assessments will be conducted to aid interpretation of results.
ETHICS AND DISSEMINATION: The results of this review will be disseminated through a peer-reviewed scientific manuscript and at international scientific conferences. Results will inform quality improvement strategies, replication of identified good practices, potential policy changes, and future research.
PROSPERO REGISTRATION NUMBER: CRD42016040053.
OBJECTIVES: To determine a CE threshold for health care interventions in Malaysia.
METHODS: A cross-sectional, contingent valuation study was conducted using a stratified multistage cluster random sampling technique in four states in Malaysia. One thousand thirteen respondents were interviewed in person for their socioeconomic background, quality of life, and WTP for a hypothetical scenario.
RESULTS: The CE thresholds established using the nonparametric Turnbull method ranged from MYR12,810 to MYR22,840 (~US $4,000-US $7,000), whereas those estimated with the parametric interval regression model were between MYR19,929 and MYR28,470 (~US $6,200-US $8,900). Key factors that affected the CE thresholds were education level, estimated monthly household income, and the description of health state scenarios.
CONCLUSIONS: These findings suggest that there is no single WTP value for a quality-adjusted life-year. The CE threshold estimated for Malaysia was found to be lower than the threshold value recommended by the World Health Organization.