uci甲状腺疾病逻辑回归模型

UCI甲状腺疾病数据集是一个二元分类问题，其中包含了甲状腺疾病患者和非患者的数据。可以使用逻辑回归模型进行建模和预测。

以下是使用Python中的Scikit-learn库实现逻辑回归模型的示例代码：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# 读取数据集
data = pd.read_csv('thyroid.csv')

# 将类别变量转换为数值变量
data['sex'] = pd.factorize(data['sex'])[0]
data['on_thyroxine'] = pd.factorize(data['on_thyroxine'])[0]
data['query_on_thyroxine'] = pd.factorize(data['query_on_thyroxine'])[0]
data['on_antithyroid_medication'] = pd.factorize(data['on_antithyroid_medication'])[0]
data['sick'] = pd.factorize(data['sick'])[0]
data['pregnant'] = pd.factorize(data['pregnant'])[0]
data['thyroid_surgery'] = pd.factorize(data['thyroid_surgery'])[0]
data['query_hypothyroid'] = pd.factorize(data['query_hypothyroid'])[0]
data['query_hyperthyroid'] = pd.factorize(data['query_hyperthyroid'])[0]
data['lithium'] = pd.factorize(data['lithium'])[0]
data['goitre'] = pd.factorize(data['goitre'])[0]
data['tumor'] = pd.factorize(data['tumor'])[0]
data['hypopituitary'] = pd.factorize(data['hypopituitary'])[0]
data['psych'] = pd.factorize(data['psych'])[0]
data['TSH_measured'] = pd.factorize(data['TSH_measured'])[0]
data['T3_measured'] = pd.factorize(data['T3_measured'])[0]
data['TT4_measured'] = pd.factorize(data['TT4_measured'])[0]
data['T4U_measured'] = pd.factorize(data['T4U_measured'])[0]
data['FTI_measured'] = pd.factorize(data['FTI_measured'])[0]
data['TBG_measured'] = pd.factorize(data['TBG_measured'])[0]
data['referral_source'] = pd.factorize(data['referral_source'])[0]

# 划分数据集
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 拟合逻辑回归模型
model = LogisticRegression()
model.fit(X_train, y_train)

# 预测
y_pred = model.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")

需要注意的是，数据集中包含一些缺失值，需要进行处理。另外，该数据集中的特征较多，可能需要进行特征选择或降维等操作，以提高模型的性能。