Machine learning for predicting Chagas disease infection in rural areas of Brazil.

INTRODUCTION: Chagas disease is a severe parasitic illness that is prevalent in Latin America and often goes unaddressed. Early detection and treatment are critical in preventing the progression of the illness and its associated life-threatening complications. In recent years, machine learning algorithms have emerged as powerful tools for disease prediction and diagnosis. METHODS: In this study, we developed machine learning algorithms to predict the risk of Chagas disease based on five general factors: age, gender, history of living in a mud or wooden house, history of being bitten by a triatomine bug, and family history of Chagas disease. We analyzed data from the Retrovirus Epidemiology Donor Study (REDS) to train five popular machine learning algorithms. The sample comprised 2,006 patients, divided into 75% for training and 25% for testing algorithm performance. We evaluated the model performance using precision, recall, and AUC-ROC metrics. RESULTS: The Adaboost algorithm yielded an AUC-ROC of 0.772, a precision of 0.199, and a recall of 0.612. We simulated the decision boundary using various thresholds and observed that in this dataset a threshold of 0.45 resulted in a 100% recall. This finding suggests that employing such a threshold could potentially save 22.5% of the cost associated with mass testing of Chagas disease. CONCLUSION: Our findings highlight the potential of applying machine learning to improve the sensitivity and effectiveness of Chagas disease diagnosis and prevention. Furthermore, we emphasize the importance of integrating socio-demographic and environmental factors into neglected disease prediction models to enhance their performance.

Reproductive factors and age at natural menopause: A systematic review and meta-analysis.

OBJECTIVES: To systematically review the evidence on the association between age at natural menopause (NM) and reproductive factors such as age at menarche, parity and ever use of oral contraceptives. STUDY DESIGN: A literature search was carried out in PubMed, Scielo, Scopus and LILACS databases, without restriction of publication year until July 6, 2017. We excluded clinical trials, case-control studies, case reports and studies using statistical methods other than Cox proportional hazard models to assess the factors associated with age at NM. Cross-sectional studies evaluating women aged <50 years were also excluded. Random-effects models were used to pool the estimates. We registered the systematic review in the International Prospective Register of Systematic Review (PROSPERO) in August 2018, CRD42018099105. RESULTS: We identified 30 articles to include in the meta-analysis. We found that previous ever use of oral contraceptives (OC) (HR = 0.87, CI = 0.82, 0.93), age at menarche ≥13 years (HR = 0.90, CI = 0.84, 0.96), and having at least one live birth (HR = 0.79, CI = 0.74, 0.85) were associated with a later age of NM. CONCLUSIONS: Despite differences in results between countries and study design, our findings suggest that previous use of OC, age at menarche ≥13 and having at least one live birth are associated with later menopause. The results suggest that these factors could be markers of later ovarian aging.