人工智能系统来帮助医生诊断肺癌问题建模多种代码
时间: 2023-12-24 09:03:01 浏览: 43
可以使用机器学习算法来建立肺癌诊断模型。以下是一些可能有用的代码:
1. 数据预处理
```
import pandas as pd
from sklearn.preprocessing import LabelEncoder
# 导入数据
data = pd.read_csv('lung_cancer_data.csv')
# 删除无关特征
data = data.drop(['Patient Id', 'Patient Age'], axis=1)
# 将分类变量转换为数字
le = LabelEncoder()
data['Gender'] = le.fit_transform(data['Gender'])
data['Smoking'] = le.fit_transform(data['Smoking'])
data['Yellow Fingers'] = le.fit_transform(data['Yellow Fingers'])
data['Shortness of Breath'] = le.fit_transform(data['Shortness of Breath'])
data['Chest Pain'] = le.fit_transform(data['Chest Pain'])
data['Wheezing'] = le.fit_transform(data['Wheezing'])
data['Fatigue'] = le.fit_transform(data['Fatigue'])
data['Allergy'] = le.fit_transform(data['Allergy'])
data['Snoring'] = le.fit_transform(data['Snoring'])
# 分割数据
X = data.drop(['Result'], axis=1)
y = data['Result']
```
2. 模型训练
```
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
# 分割数据集为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# 训练模型
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
# 预测测试集结果
y_pred = model.predict(X_test)
# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: ", accuracy)
```
3. 模型评估
```
from sklearn.metrics import classification_report, confusion_matrix
# 预测训练集结果
y_train_pred = model.predict(X_train)
# 打印混淆矩阵和分类报告
print("Confusion Matrix:\n", confusion_matrix(y_train, y_train_pred))
print("Classification Report:\n", classification_report(y_train, y_train_pred))
```
以上代码可以帮助您构建一个简单的肺癌诊断模型,但是请注意,这只是一个基础模型,需要根据实际情况进行调整和优化。
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