Background Communicable diseases represent a huge economic burden for healthcare systems and for society. Sexually transmitted infections (STIs) are a concerning issue, especially in developing and underdeveloped countries, in which environmental factors and other determinants of health play a role in contributing to its fast spread. In light of this situation, machine learning techniques have been explored to assess the incidence of syphilis and contribute to the epidemiological surveillance in this scenario. Objective The main goal of this work is to evaluate the performance of different machine learning models on predicting undesirable outcomes of congenital syphilis in order to assist resources allocation and optimize the healthcare actions, especially in a constrained health environment. Method We use clinical and sociodemographic data from pregnant women that were assisted by a social program in Pernambuco, Brazil, named Ma˜e Coruja Pernambucana Program (PMCP). Based on a rigorous methodology, we propose six experiments using three feature selection techniques to select the most relevant attributes, pre-process and clean the data, apply hyperparameter optimization to tune the machine learning models, and train and test models to have a fair evaluation and discussion. Results The AdaBoost-BODS-Expert model, an Adaptive Boosting (AdaBoost) model that used attributes selected by health experts, presented the best results in terms of evaluation metrics and acceptance by health experts from PMCP. By using this model, the results are more reliable and allows adoption on a daily usage to classify possible outcomes of congenital syphilis using clinical and sociodemographic data.

IntroductionThe long average incubation time from HIV infection to AIDS makes it difficult to estimate the recent tendencies of HIV from AIDS incidence data. The objective of this study was to investigate the effects of three temporal components in AIDS incidence in the state of Rio de Janeiro, Brazil - age, period, and cohort.MethodsAge-specific AIDS incidence rates per 100,000 from Rio de Janeiro (Brazil) were calculated for both sexes using five-year age classes from 1985 to 2009 based on reported data from the Notifiable Disease Information System of the Brazilian Ministry of Health and from census population counts. Multivariate negative binomial models were used to analyze the risk of AIDS by age, period, and birth cohort.ResultsFrom 1985 to 2009, AIDS incidence initially increased with age in each birth cohort and then decreased (except for individuals born from 1971–1979 to 1986–1994). High peaks in the rates in each birth cohort were detected in 1995–1999 for males and in 2000–2004 for females. Multivariate analysis showed the maximum risk of AIDS in the 30–34 age group and 1958–1962 birth cohort.ConclusionAge, birth cohort, and period effects all may have influenced the AIDS incidence rates over the period investigated. From 1985 to 1999, comparison of the tendencies (by age) of the period with the birth cohort revealed opposing tendencies in individuals older than 29 years and in the youngest age groups (0 to 14 years). From 2000 to 2009, a strong age effect can be observed in both sexes. Consistent changes in period tendency curves suggest the occurrence of period effects. A reduction in the intensity of the risk of AIDS can be observed after 2000–2004.

Background Communicable diseases represent a huge economic burden for healthcare systems and for society. Sexually transmitted infections (STIs) are a concerning issue, especially in developing and underdeveloped countries, in which environmental factors and other determinants of health play a role in contributing to its fast spread. In light of this situation, machine learning techniques have been explored to assess the incidence of syphilis and contribute to the epidemiological surveillance in this scenario. Objective The main goal of this work is to evaluate the performance of different machine learning models on predicting undesirable outcomes of congenital syphilis in order to assist resources allocation and optimize the healthcare actions, especially in a constrained health environment. Method We use clinical and sociodemographic data from pregnant women that were assisted by a social program in Pernambuco, Brazil, named Mãe Coruja Pernambucana Program (PMCP). Based on a rigorous methodology, we propose six experiments using three feature selection techniques to select the most relevant attributes, pre-process and clean the data, apply hyperparameter optimization to tune the machine learning models, and train and test models to have a fair evaluation and discussion. Results The AdaBoost-BODS-Expert model, an Adaptive Boosting (AdaBoost) model that used attributes selected by health experts, presented the best results in terms of evaluation metrics and acceptance by health experts from PMCP. By using this model, the results are more reliable and allows adoption on a daily usage to classify possible outcomes of congenital syphilis using clinical and sociodemographic data.