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Analyze another fileLogistic regression with adjusted odds ratios and XGBoost feature importance to identify stroke risk factors across clinical and demographic variables
Use when you have patient-level data with a binary stroke outcome and want to quantify which risk factors independently elevate stroke probability
Do not use for time-series survival analysis, very small samples under 100 patients, or when causal inference rather than association is needed
Dataset with 12 columns
Minimum 100 rows
Cornerstone #13 — stroke risk prediction with logistic + xgboost on fedesoriano/stroke-prediction-dataset (3,533 votes)
Mean clinical values (age, glucose, BMI, hypertension, heart disease) by stroke vs no-stroke group
Age distribution comparison between stroke and non-stroke patients
Raw stroke incidence rates by clinical and demographic subgroups
Adjusted odds ratios with confidence intervals from logistic regression
XGBoost feature importance ranking of stroke predictors
Correlation matrix of numeric risk factors to detect multicollinearity
Stroke prevalence by smoking status category
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
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