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https://www.selleckchem.com/products/danicopan.html OBJECTIVE To evaluate the performance of machine learning (ML) algorithms and to compare them to logistic regression for the prediction of risk of cardiovascular diseases (CVD), chronic kidney disease (CKD), diabetes (DM), and hypertension (HTN) and in a prospective cohort study using simple clinical predictors. STUDY DESIGN AND SETTING We conducted analyses in a population-based cohort study in Asian adults (n=6,762). Five different ML models were considered single-hidden-layer neural network, support vector machine, random forest, gradient boosting machine and k-nearest neighbour, and were compared to standard logistic regression. RESULTS The incidences at 6-year of CVD, CKD, DM, and HTN cases were 4.0%, 7.0%, 9.2%, and 34.6%, respectively. Logistic regression reached the highest AUC for CKD (0.905 [0.88, 0.93]) and DM (0.768 [0.73, 0.81]) predictions. For CVD and HTN, the best models were neural network (0.753 [0.70, 0.81]) and support vector machine (0.780 [0.747, 0.812]), respectively. However, the differences with logistic regression were small (less than 1%) and non-significant. Logistic regression, gradient boosting machine and neural network were systematically ranked among the best models. CONCLUSION Logistic regression yields as good performance as ML models to predict the risk of major chronic diseases with low incidence and simple clinical predictors. OBJECTIVE Help educators address misconceptions about P-values and provide a tool that can be used to teach a more contemporary interpretation. DESIGN and Setting A scripted tutorial utilizing problem-based learning and a diagnostic test analogy to deconstruct the misunderstandings about P-values and develop a more Bayesian approach to study interpretation. RESULTS A diagnostic test analogy is an effective teaching tool. Learners' understanding of Bayes' theorem in diagnostic testing can be used as a bridge to the realization that the pre-study probabilit

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