影像组学评分代码python
时间: 2023-09-03 18:15:55 浏览: 157
影像组学评分是指使用图像处理和机器学习技术对医学影像进行自动化评分,常用于辅助医生进行诊断和治疗。下面是一个基于Python的影像组学评分代码示例:
首先,需要导入必要的包和库:
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
```
接着,我们读取数据集并进行数据预处理:
```python
# 读取数据集
data = pd.read_csv('dataset.csv')
# 去除无用特征
data = data.drop(['id'], axis=1)
# 处理缺失值
data = data.fillna(data.mean())
# 将标签转化为数值类型
data['diagnosis'] = [1 if i=='M' else 0 for i in data['diagnosis']]
```
然后,我们将数据集划分为训练集和测试集,并进行特征缩放:
```python
# 划分数据集为训练集和测试集
X = data.drop(['diagnosis'], axis=1)
y = data['diagnosis']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# 特征缩放
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
```
接着,我们定义模型并进行训练和预测:
```python
# 定义模型
model = svm.SVC(kernel='linear')
model.fit(X_train, y_train)
# 预测测试集
y_pred = model.predict(X_test)
```
最后,我们对模型进行评估并输出结果:
```python
# 输出评估结果
accuracy = accuracy_score(y_test, y_pred)
confusion = confusion_matrix(y_test, y_pred)
classification = classification_report(y_test, y_pred)
print('Accuracy:', accuracy)
print('Confusion Matrix:\n', confusion)
print('Classification Report:\n', classification)
```
以上就是一个简单的影像组学评分代码示例,你可以根据自己的需求进行修改和优化。
阅读全文