由于没有提供数据集，以下是伪代码实现：

导入所需库

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.svm import SVC from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve

读取数据集

data = pd.read_csv('data.csv')

特征选择

X = data[['gender', 'age', 'BMI']] y = data['disease']

划分训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

建立支持向量机模型

clf = SVC(kernel='linear', probability=True)

训练模型

clf.fit(X_train, y_train)

预测测试集

y_pred = clf.predict(X_test) y_prob = clf.predict_proba(X_test)[:, 1]

计算准确率和AUC

accuracy = accuracy_score(y_test, y_pred) auc_score = roc_auc_score(y_test, y_prob)

绘制ROC曲线

fpr, tpr, thresholds = roc_curve(y_test, y_prob) plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % auc_score) plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Receiver operating characteristic') plt.legend(loc="lower right") plt.show()

输出准确率和AUC

print('准确率：', accuracy) print('AUC：', auc_score