用python代码 实现癫痫数据的分类与预测

可以使用sklearn库中的机器学习算法进行癫痫数据的分类与预测，例如可以使用支持向量机(SVM)或者人工神经网络(ANN)等算法。以下是示例代码：

# 数据预处理
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

data = pd.read_csv('epilepsy_data.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# 特征缩放
scaler = StandardScaler()
X = scaler.fit_transform(X)

# 训练集和测试集的分割
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# SVM分类器
from sklearn.svm import SVC

classifier = SVC(kernel='linear', random_state=0)
classifier.fit(X_train, y_train)

# 预测结果
y_pred = classifier.predict(X_test)

# ANN分类器
from tensorflow import keras

ann = keras.models.Sequential([
keras.layers.Dense(32, activation='relu', input_shape=(X_train.shape[1],)),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dense(1, activation='sigmoid')
])

ann.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
ann.fit(X_train, y_train, epochs=100, batch_size=32)

# 预测结果
y_pred = ann.predict(X_test)
y_pred = (y_pred > 0.5)

# 输出结果
print("SVM Accuracy:", accuracy_score(y_test, y_pred))
print("ANN Accuracy:", accuracy_score(y_test, y_pred))