SVM影像组学特征

近期做一个影像组学的分类模型

做的是一个胃癌T分期的模型，我刷选统计出一些胃癌区域的特征，如图：有癌症面积、体积等等

下面要做一个SVM（支持向量机）分类的模型，导入该文件，进行二分类，代码如下：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import classification_report, confusion_matrix# 加载数据
data_path = '../data/data.xlsx'
data = pd.read_excel(data_path, sheet_name='class2T2_T34')# 准备数据
X = data.drop(['Filename', 'label'], axis=1)  # 删除非特征列
y = data['label']  # 标签列# 分割数据为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# 特征缩放
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)# 创建SVM分类器# 线性核
# svm_classifier = SVC(kernel='linear', decision_function_shape='ovo')
# 多项式核（Polynomial Kernel）
svm_classifier = SVC(kernel='poly', degree=3, coef0=1, decision_function_shape='ovo')
# 径向基函数核（Radial Basis Function, RBF Kernel）
# svm_classifier = SVC(kernel='rbf', gamma='scale', decision_function_shape='ovo')
# Sigmoid核
# svm_classifier = SVC(kernel='sigmoid', coef0=1, decision_function_shape='ovo')# 训练模型
svm_classifier.fit(X_train, y_train)# 预测测试集结果
y_pred = svm_classifier.predict(X_test)# 评估模型
print("Confusion Matrix:")
print(confusion_matrix(y_test, y_pred))
print("\nClassification Report:")
print(classification_report(y_test, y_pred))

运行结果如下：