显示结果：PermissionError: [Errno 13] Permission denied: 'C:\Users\1\Desktop\heart_disease'

• ```

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

数据加载

data = pd.read_csv(r"C:\Users\1\Desktop\heart_disease")

分离特征和目标变量

y = data['target'].values
X = data.drop('target', axis=1).values

数据预处理

scaler = StandardScaler()
X = scaler.fit_transform(X)

数据划分

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

构建模型

model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(X_train.shape[1],)))
model.add(Dense(64, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

模型编译

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

模型训练

model.fit(X_train, y_train, epochs=10, batch_size=32)

模型评估

score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

新数据预测

new_data = np.array([[65, 1, 3, 140, 260, 0, 2, 140, 1, 2.0, 3, 0, 6]])
new_data = scaler.transform(new_data)
prediction = model.predict(new_data)
if prediction[0][0] >= 0.5:
print('有心血管疾病')
else:
print('无心血管疾病')

```

PermissionError: [Errno 13] Permission denied: 'C:\Users\1\Desktop\heart_disease'
没有权限，用管理员权限运行程序