Fig. 3: The composite figure of detailed analyses of the most important predictors of the best performing XGB model and their link to seasonal distribution of daily AIS admissions.
From: Machine learning-based forecasting of daily acute ischemic stroke admissions using weather data
a Horizontal bar chart of the top ten most relevant features using normalized gain-based variable importance ranking of the best XGB model. b Shapley additive explanations (SHAP) of the top six variables, including (upper-row) maximal air pressure (Pmax), lagged 1- and 2-days maximal wind speed (Vmax_lag2) and wind gust speeds (Vgust_lag1); and (lower-row) minimal lagged 3-days temperature (Tmin_lag3), minimal perceived temperature (PTmin), and 7-days minimum temperature (Tmin_lag7). These variables accounted for an overall sum of 0.84 gain-based importance out of the 133 investigated weather and calendar features. Inflection points on the subplots indicate (gray dashed lines) when the respective variable’s effect was associated with an increase or decrease in stroke counts. c Faceted heatmaps indicating the seasonal distributions of weather in the training data (2015–2020; n = 2190 days), thresholded using respective values from SHAP inflection points. The number of days that the respective condition has occurred was calculated by jointly aggregating at yearly and weekly levels (Supplementary Note 6. Aggregation methodology for the heatmap in Fig. 3, pp. 4). Protective (shades of blue) or harmful (red) median number of days were then color-coded based on the sign of the SHAP values. Additionally, the deltas of weekly stroke counts (aggregated over the seven years) were compared against the respective quarterly medians (lower right corner). Pmax showed a sigmoid-like link as low pressures (Pmax < 960 hPa) substantially decreased stroke admissions (SHAP = −0.95), while medium-high values (974–1013 hPa) were associated with an increased stroke incidence all year round (Q1–Q4). Cold stressor days (Q1, Q2, and Q4) and associated windy conditions (Vmax_lag2 \(\ge\)10.4 m/s and Vgust_max_lag1 \(\ge\)14 m/s) substantially increased admissions (SHAPVmax = 0.11 and SHAPVgust = 0.45). Similarly, extended cold stressor periods during winter with Tmin_lag3 < −2 °C or PTmin < −1.4 °C were strongly linked to more strokes (SHAP up to 1.47). Conversely, PTmin in classical temperate ranges (−1.4 < PTmin < 20 °C) were slightly protective (SHAP = −0.03), although these effects could be outweighed (SHAPTmin_lag7 = 1.18) during extended heat stress periods (Tmin_lag7 \(\ge\)15°C) of the summer.
