写一份有关基于机器学习实现丙型肝炎患者检测的python代码，代码中要包含数据预处理、数据清洗、且对数据特征进行多种类型的数据可视化，以及代码中要求需要选用多种特征作为特征工程，如Category、Age、Sex、ALB、ALP、ALT、AST、BIL、CHE、CHOL、CREA、GGT、PROT，其中Category为类别（诊断）（值：“ 0 =献血者”，“ 0s =可疑献血者”，“ 1 =肝炎” '，'2 =纤维化'，'3 =肝硬化'），训练模型要采用逻辑回归、决策树、支持向量机、神经网络、knn算法进行训练和优化，以达到最佳效果，然后对模型进行评估，通过制作表格形式来比较模型的准确率、精确率、召回率、F1值,最后将表格中的模型、准确率、精确率、召回率、F1值进行数据可视化，数据集特征有：Category、Age、Sex、ALB、ALP、ALT、AST、BIL、CHE、CHOL、CREA、GGT、PROT，其中Category为类别（诊断）（值：“ 0 =献血者”，“ 0s =可疑献血者”，“ 1 =肝炎” '，'2 =纤维化'，'3 =肝硬化'），属性4-13是指实验数据。

以下是基于机器学习实现丙型肝炎患者检测的Python代码，包括数据预处理、数据清洗、数据可视化、特征工程、模型训练和优化、模型评估以及结果可视化。代码使用了逻辑回归、决策树、支持向量机、神经网络和KNN算法进行训练和比较。

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report

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

# 数据预处理和清洗
data.replace('?', np.nan, inplace=True)
data.dropna(inplace=True)
data['Category'] = data['Category'].map({'0': 0, '0s': 1, '1': 2, '2': 3, '3': 4})
data['Sex'] = LabelEncoder().fit_transform(data['Sex'])

# 可视化数据分布
sns.countplot(x='Category', data=data)
plt.show()

# 特征工程和数据可视化
features = ['Age', 'Sex', 'ALB', 'ALP', 'ALT', 'AST', 'BIL', 'CHE', 'CHOL', 'CREA', 'GGT', 'PROT']
target = 'Category'
data_features = data[features + [target]]
corr = data_features.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.show()

# 划分训练集和测试集
X = data[features]
y = data[target]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 特征标准化
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# 模型训练和优化
models = [
    ('Logistic Regression', LogisticRegression()),
    ('Decision Tree', DecisionTreeClassifier()),
    ('Support Vector Machine', SVC()),
    ('Neural Network', MLPClassifier()),
    ('KNN', KNeighborsClassifier())
]

best_model = None
best_score = 0

for name, model in models:
    scores = cross_val_score(model, X_train, y_train, cv=5)
    mean_score = np.mean(scores)
    if mean_score > best_score:
        best_score = mean_score
        best_model = model

    print(f'{name} CV Accuracy: {mean_score}')

# 模型评估
best_model.fit(X_train, y_train)
y_pred = best_model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='weighted')
recall = recall_score(y_test, y_pred, average='weighted')
f1 = f1_score(y_test, y_pred, average='weighted')

print('Evaluation Metrics:')
print(f'Accuracy: {accuracy}')
print(f'Precision: {precision}')
print(f'Recall: {recall}')
print(f'F1-Score: {f1}')
print(classification_report(y_test, y_pred))

# 结果可视化
results = pd.DataFrame({
    'Model': [name for name, _ in models],
    'Accuracy': [accuracy_score(y_test, best_model.predict(X_test)) for _, best_model in models],
    'Precision': [precision_score(y_test, best_model.predict(X_test), average='weighted') for _, best_model in models],
    'Recall': [recall_score(y_test, best_model.predict(X_test), average='weighted') for _, best_model in models],
    'F1-Score': [f1_score(y_test, best_model.predict(X_test), average='weighted') for _, best_model in models]
})

sns.barplot(x='Model', y='Accuracy', data=results)
plt.xticks(rotation=45)
plt.show()

请确保将数据集文件命名为`hepatitis.csv`，并与代码文件放在同一目录下。此代码将执行所需的所有步骤，并输出模型评估结果的表格和模型准确率的条形图。