由于没有提供数据集，以下是基本的代码框架：

导入必要的库

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, roc_curve, auc import matplotlib.pyplot as plt

读取数据集

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

将性别转换为0和1

data['sex'] = data['sex'].apply(lambda x: 1 if x == 'Male' else 0)

划分训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(data[['sex', 'BMI']], data['disease'], test_size=0.2, random_state=42)

建立随机森林模型

rf = RandomForestClassifier(n_estimators=100, random_state=42) rf.fit(X_train, y_train)

在测试集上进行预测

y_pred = rf.predict(X_test)

计算准确率

accuracy = accuracy_score(y_test, y_pred) print('Accuracy:', accuracy)

计算AUC

fpr, tpr, thresholds = roc_curve(y_test, y_pred) roc_auc = auc(fpr, tpr) print('AUC:', roc_auc)

绘制ROC曲线

plt.figure() plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc) 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()

评估预测效果

...根据实际情况进行评