These 12 variables were then ranked for importance by three machine learning methods, and top five variables were found including calcium, CPK, anion gap, bicarbonate, monocyte (Figure 4A) for ababoost, a top five ranking of calcium, CPK, stroke, monocyte, INR (Figure 4B) obtained by the Xgboost algorithm, and a top five ranking of calcium, CPK, monocyte, EGFR, anion gap (Figure 4C) obtained by the Random Forest algorithm. This evidence concerns the gene PIK3C2A and Stroke.