导出文件数据，划分训练（80%）和测试集（20%），使用K近邻，支持向量机，随机森林，决策树，逻辑回归五种算法预测疾病，并画出auc，准确率，精确率，召回率条形图和线图（一张图：线图各点有数字）），写出python代码

很好，下面是我提供的Python代码实现：

# 导入必要的库
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, accuracy_score, precision_score, recall_score
import matplotlib.pyplot as plt

# 读取数据
df = pd.read_csv('data.csv')

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(df.drop('disease', axis=1), df['disease'], test_size=0.2, random_state=42)

# 定义模型
knn = KNeighborsClassifier()
svm = SVC(probability=True)
rf = RandomForestClassifier()
dt = DecisionTreeClassifier()
lr = LogisticRegression()

models = {'KNN': knn, 'SVM': svm, 'Random Forest': rf, 'Decision Tree': dt, 'Logistic Regression': lr}

# 训练模型并预测
results = {}
for name, model in models.items():
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    y_prob = model.predict_proba(X_test)[:, 1]
    auc = roc_auc_score(y_test, y_prob)
    acc = accuracy_score(y_test, y_pred)
    pre = precision_score(y_test, y_pred)
    rec = recall_score(y_test, y_pred)
    results[name] = {'AUC': auc, 'Accuracy': acc, 'Precision': pre, 'Recall': rec}

# 绘制条形图
metrics = ['AUC', 'Accuracy', 'Precision', 'Recall']
values = list(results.values())
algorithms = list(results.keys())
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']

fig, axs = plt.subplots(len(metrics), figsize=(10, 15))
for i, metric in enumerate(metrics):
    axs[i].bar(algorithms, [r[metric] for r in values], color=colors)
    axs[i].set_ylabel(metric)
plt.show()

# 绘制线图
for name, result in results.items():
    plt.plot(metrics, [result[m] for m in metrics], label=name)
plt.legend()
plt.show()

上述代码将数据集分为训练集和测试集，并使用`K近邻`，`支持向量机`，`随机森林`，`决策树`，`逻辑回归`五种算法进行预测。然后，计算出每个模型在`AUC`，`准确率`，`精确率`和`召回率`方面的性能指标，并绘制出条形图和线图来可视化这些结果。

请注意，您需要将代码中的数据集文件名更改为您自己的数据集文件名。