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Fig. 4 | Arthritis Research & Therapy

Fig. 4

From: Machine learning-based prediction model for responses of bDMARDs in patients with rheumatoid arthritis and ankylosing spondylitis

Fig. 4

Result of feature importance analysis from the best performing models of each machine learning method. The X-axis represents the input clinical features. The Y-axis represents the feature importance score calculated using the Gini importance or risk backpropagation methods in RF-method/XGBoost and ANN, respectively. The color of columns represents the categories in which the feature was included. Top 20 important features are shown in figures. Feature importance of a RF-method model, b XGBoost model, and c ANN model in patients with RA. Feature importance of d RF-method model, e XGBoost model, and f ANN model in patients with AS. WBC, white cell count; BMI, body mass index; Plt, platelet; Hb, hemoglobin; Hct, hematocrit; DM, diabetes mellitus; anti-CCP, anti-cyclic citrullinated protein; ILD, interstitial lung disease; MTX, methotrexate; TACRO, tacrolimus; LEFL, leflunomide; SSZ, sulfasalazine

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