gnguy/mph_thesis_ml

MPH Thesis: Determinants of In-Hospital Child Mortality in Uganda # mph_thesis_ml The original paper used logistic regression using backwards selection on a number of binary variables to attempt to predict the risk of childhood mortality. It created a simple binary risk score to aid in calculating a child's probability of death. My work attempts to extend this analysis by using machine learning analyses to validate this approach and compare it to other analytical methods. In particular, I aim to produce some decision trees to aid in decision-making. 01_clean_data.do: * Formats all variables the same way * Decide what to do with “9” values * Exclude variables that don’t matter * Create variable lists: signs, symptoms, treatment, diagnoses, test results, outcomes * Turn all variables into binary indicators * May need Herbie’s guidance on indicators not included in the original analysis 02_prep_data.R * Apply different methods of imputation or observation-dropping * Output table of missing variables for all variables of interest * Descriptive table of primary patient characteristics * Any other descriptive stats analysis_functions.R * Specify one function for each method — assume same data structure, and take in arguments for formula etc. * Logistic regression with backwards selection * Define variable importance cutoffs for selection (or default) * Decision trees * Define ideal tree breakdown — pruning characteristics etc. * Random forests * Source up-sampling/down-sampling methods * Define sampling parameters, number of cv runs, etc. * Output predictions, graphical representations, ROC analyses. * Save graphs, predictions, and ROC analyses to flat files (how to toggle by source?) 03_apply_analysis.R * Source all analysis functions * Separate data into train and test data * Apply analysis functions and get/plot results