Fig. 4 | Scientific Reports

Fig. 4

From: Large language models versus classical machine learning performance in COVID-19 mortality prediction using high-dimensional tabular data

Fig. 4

SHAP analysis comparing feature importance in COVID-19 mortality prediction models. (a) Average global feature impact for classical machine learning (CML) models. (b) Global impact scores for XGBoost (best-performing CML). (c) SHAP score distribution for XGBoost. (d) Average global feature impact for large language models (LLMs). (e) Global impact scores for GPT-4 (best-performing LLM). (f) SHAP score distribution for GPT-4. Key features include age, O2 saturation (VS - O2 Sat), and creatinine (Cr) levels. In panels c and f, red indicates higher feature values; positive SHAP values increase mortality prediction, negative values decrease it. VS: vital sign.

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