Fig. 3: Feature importance ranked by SHAP and interpretation of personalized predictions of depression risk using SHAP values and equation. | Translational Psychiatry

Fig. 3: Feature importance ranked by SHAP and interpretation of personalized predictions of depression risk using SHAP values and equation.

From: An interpretable machine learning model predicts the interactive and cumulative risks of different environmental chemical exposures on depression

Fig. 3

AThis panel illustrates the ranking of mean absolute SHAP values for each feature in the model, reflecting their overall importance in predicting depression. B SHAP Summary Plot: This figure shows the distribution of SHAP values for individual predictions, with positive SHAP values (red) indicating an increase in the prediction of depression and negative SHAP values (blue) indicating a decrease. Features with higher SHAP values have a more significant impact on the model’s prediction, demonstrating the cumulative effect of feature contributions on the final output. Personalized depression risk can then be predicted using the following equation: Base value + ∑ (SHAP value feature × feature value). Base Value: The average model output across the entire dataset before accounting for specific feature contributions. Feature Value: The actual measured value of each feature for an individual.

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