第R9周：阿尔茨海默病诊断（优化特征选择版）

• 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
• 🍖 原作者：K同学啊

1. 导入数据

import pandas as pd  
import numpy as np 
import matplotlib.pyplot as plt  
import seaborn as sns 
import torch  
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDatasetplt.rcParams["font.sans-serif"] = ["Microsoft YaHei"]  # 显示中文
plt.rcParams['axes.unicode_minus'] = False		# 显示负号data_df = pd.read_csv("alzheimers_disease_data.csv")data_df.head()

	PatientID	Age	Gender	Ethnicity	EducationLevel	BMI	Smoking	AlcoholConsumption	PhysicalActivity	DietQuality	...	MemoryComplaints	BehavioralProblems	ADL	Confusion	Disorientation	PersonalityChanges	DifficultyCompletingTasks	Forgetfulness	Diagnosis	DoctorInCharge
0	4751	73	0	0	2	22.927749	0	13.297218	6.327112	1.347214	...	0	0	1.725883	0	0	0	1	0	0	XXXConfid
1	4752	89	0	0	0	26.827681	0	4.542524	7.619885	0.518767	...	0	0	2.592424	0	0	0	0	1	0	XXXConfid
2	4753	73	0	3	1	17.795882	0	19.555085	7.844988	1.826335	...	0	0	7.119548	0	1	0	1	0	0	XXXConfid
3	4754	74	1	0	1	33.800817	1	12.209266	8.428001	7.435604	...	0	1	6.481226	0	0	0	0	0	0	XXXConfid
4	4755	89	0	0	0	20.716974	0	18.454356	6.310461	0.795498	...	0	0	0.014691	0	0	1	1	0	0	XXXConfid

5 rows × 35 columns

# 标签中文化
data_df.rename(columns={ "Age": "年龄", "Gender": "性别", "Ethnicity": "种族", "EducationLevel": "教育水平", "BMI": "身体质量指数（BMI）", "Smoking": "吸烟状况", "AlcoholConsumption": "酒精摄入量", "PhysicalActivity": "体育活动时间", "DietQuality": "饮食质量评分", "SleepQuality": "睡眠质量评分", "FamilyHistoryAlzheimers": "家族阿尔茨海默病史", "CardiovascularDisease": "心血管疾病", "Diabetes": "糖尿病", "Depression": "抑郁症史", "HeadInjury": "头部受伤", "Hypertension": "高血压", "SystolicBP": "收缩压", "DiastolicBP": "舒张压", "CholesterolTotal": "胆固醇总量", "CholesterolLDL": "低密度脂蛋白胆固醇（LDL）", "CholesterolHDL": "高密度脂蛋白胆固醇（HDL）", "CholesterolTriglycerides": "甘油三酯", "MMSE": "简易精神状态检查（MMSE）得分", "FunctionalAssessment": "功能评估得分", "MemoryComplaints": "记忆抱怨", "BehavioralProblems": "行为问题", "ADL": "日常生活活动（ADL）得分", "Confusion": "混乱与定向障碍", "Disorientation": "迷失方向", "PersonalityChanges": "人格变化", "DifficultyCompletingTasks": "完成任务困难", "Forgetfulness": "健忘", "Diagnosis": "诊断状态", "DoctorInCharge": "主诊医生" },inplace=True)data_df.columns

Index(['PatientID', '年龄', '性别', '种族', '教育水平', '身体质量指数（BMI）', '吸烟状况', '酒精摄入量','体育活动时间', '饮食质量评分', '睡眠质量评分', '家族阿尔茨海默病史', '心血管疾病', '糖尿病', '抑郁症史','头部受伤', '高血压', '收缩压', '舒张压', '胆固醇总量', '低密度脂蛋白胆固醇（LDL）','高密度脂蛋白胆固醇（HDL）', '甘油三酯', '简易精神状态检查（MMSE）得分', '功能评估得分', '记忆抱怨', '行为问题','日常生活活动（ADL）得分', '混乱与定向障碍', '迷失方向', '人格变化', '完成任务困难', '健忘', '诊断状态','主诊医生'],dtype='object')

2. 数据处理

data_df.isnull().sum()

PatientID           0
年龄                  0
性别                  0
种族                  0
教育水平                0
身体质量指数（BMI）         0
吸烟状况                0
酒精摄入量               0
体育活动时间              0
饮食质量评分              0
睡眠质量评分              0
家族阿尔茨海默病史           0
心血管疾病               0
糖尿病                 0
抑郁症史                0
头部受伤                0
高血压                 0
收缩压                 0
舒张压                 0
胆固醇总量               0
低密度脂蛋白胆固醇（LDL）      0
高密度脂蛋白胆固醇（HDL）      0
甘油三酯                0
简易精神状态检查（MMSE）得分    0
功能评估得分              0
记忆抱怨                0
行为问题                0
日常生活活动（ADL）得分       0
混乱与定向障碍             0
迷失方向                0
人格变化                0
完成任务困难              0
健忘                  0
诊断状态                0
主诊医生                0
dtype: int64

from sklearn.preprocessing import LabelEncoder# 创建 LabelEncoder 实例
label_encoder = LabelEncoder()# 对非数值型列进行标签编码
data_df['主诊医生'] = label_encoder.fit_transform(data_df['主诊医生'])data_df.head()

	PatientID	年龄	性别	种族	教育水平	身体质量指数（BMI）	吸烟状况	酒精摄入量	体育活动时间	饮食质量评分	...	记忆抱怨	行为问题	日常生活活动（ADL）得分	混乱与定向障碍	迷失方向	人格变化	完成任务困难	健忘	诊断状态	主诊医生
0	4751	73	0	0	2	22.927749	0	13.297218	6.327112	1.347214	...	0	0	1.725883	0	0	0	1	0	0	0
1	4752	89	0	0	0	26.827681	0	4.542524	7.619885	0.518767	...	0	0	2.592424	0	0	0	0	1	0	0
2	4753	73	0	3	1	17.795882	0	19.555085	7.844988	1.826335	...	0	0	7.119548	0	1	0	1	0	0	0
3	4754	74	1	0	1	33.800817	1	12.209266	8.428001	7.435604	...	0	1	6.481226	0	0	0	0	0	0	0
4	4755	89	0	0	0	20.716974	0	18.454356	6.310461	0.795498	...	0	0	0.014691	0	0	1	1	0	0	0

5 rows × 35 columns

2.1 患病占比

# 计算是否患病, 人数
counts = data_df["诊断状态"].value_counts()# 计算百分比
sizes = counts / counts.sum() * 100# 绘制环形图
fig, ax = plt.subplots()
wedges, texts, autotexts = ax.pie(sizes, labels=sizes.index, autopct='%1.2ff%%', startangle=90, wedgeprops=dict(width=0.3))plt.title("患病占比(1患病，0没有患病)")
plt.show()

2.2 相关性分析

plt.figure(figsize=(40, 35))
sns.heatmap(data_df.corr(), annot=True, fmt=".2f")
plt.show()

2.3 年龄与患病探究

data_df['年龄'].min(), data_df['年龄'].max()

(np.int64(60), np.int64(90))

# 计算每一个年龄段患病人数 
age_bins = range(60, 91)
grouped = data_df.groupby('年龄').agg({'诊断状态': ['sum', 'size']})  # 分组、聚合函数: sum求和，size总大小
grouped.columns = ['患病', '总人数']
grouped['不患病'] = grouped['总人数'] - grouped['患病']  # 计算不患病的人数# 设置绘图风格
sns.set(style="whitegrid")plt.figure(figsize=(12, 5))# 获取x轴标签（即年龄）
x = grouped.index.astype(str)  # 将年龄转换为字符串格式便于显示# 画图
plt.bar(x, grouped["不患病"], 0.35, label="不患病", color='skyblue')
plt.bar(x, grouped["患病"], 0.35, label="患病", color='salmon')# 设置标题
plt.title("患病年龄分布", fontproperties='Microsoft YaHei')
plt.xlabel("年龄", fontproperties='Microsoft YaHei')
plt.ylabel("人数", fontproperties='Microsoft YaHei')# 如果需要对图例也应用相同的字体
plt.legend(prop={'family': 'Microsoft YaHei'})# 展示
plt.tight_layout()
plt.show()

3. 特征选择

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_reportdata = data_df.copy()X = data_df.iloc[:, 1:-2]
y = data_df.iloc[:, -2]X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# 标准化
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)# 模型创建
tree = DecisionTreeClassifier()
tree.fit(X_train, y_train)
pred = tree.predict(X_test)reporter = classification_report(y_test, pred)
print(reporter)

              precision    recall  f1-score   support0       0.91      0.92      0.92       2771       0.85      0.84      0.85       153accuracy                           0.89       430macro avg       0.88      0.88      0.88       430
weighted avg       0.89      0.89      0.89       430

# 特征展示
feature_importances = tree.feature_importances_
features_rf = pd.DataFrame({'特征': X.columns, '重要度': feature_importances})
features_rf.sort_values(by='重要度', ascending=False, inplace=True)
plt.figure(figsize=(20, 10))
sns.barplot(x='重要度', y='特征', data=features_rf)
plt.xlabel('重要度')
plt.ylabel('特征')
plt.title('随机森林特征图')
plt.show()

from sklearn.feature_selection import RFE# 使用 RFE 来选择特征
rfe_selector = RFE(estimator=tree, n_features_to_select=20)  # 选择前20个特征
rfe_selector.fit(X, y)  
X_new = rfe_selector.transform(X)
feature_names = np.array(X.columns) 
selected_feature_names = feature_names[rfe_selector.support_]
print(selected_feature_names)

[‘年龄’ ‘种族’ ‘教育水平’ ‘身体质量指数（BMI）’ ‘酒精摄入量’ ‘体育活动时间’ ‘饮食质量评分’ ‘睡眠质量评分’ ‘心血管疾病’
‘收缩压’ ‘舒张压’ ‘胆固醇总量’ ‘低密度脂蛋白胆固醇（LDL）’ ‘高密度脂蛋白胆固醇（HDL）’ ‘甘油三酯’
‘简易精神状态检查（MMSE）得分’ ‘功能评估得分’ ‘记忆抱怨’ ‘行为问题’ ‘日常生活活动（ADL）得分’]

4. 构建数据集

4.1 数据集划分与标准化

feature_selection = ['年龄', '种族','教育水平','身体质量指数（BMI）', '酒精摄入量', '体育活动时间', '饮食质量评分', '睡眠质量评分', '心血管疾病','收缩压', '舒张压', '胆固醇总量', '低密度脂蛋白胆固醇（LDL）', '高密度脂蛋白胆固醇（HDL）', '甘油三酯','简易精神状态检查（MMSE）得分', '功能评估得分', '记忆抱怨', '行为问题', '日常生活活动（ADL）得分']X = data_df[feature_selection]# 标准化, 标准化其实对应连续性数据，分类数据不适合，由于特征中只有种族是分类数据，这里我偷个“小懒”
sc = StandardScaler()
X = sc.fit_transform(X)X = torch.tensor(np.array(X), dtype=torch.float32)
y = torch.tensor(np.array(y), dtype=torch.long)# 再次进行特征选择
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)X_train.shape, y_train.shape

(torch.Size([1719, 20]), torch.Size([1719]))

4.2 构建加载

batch_size = 32train_dl = DataLoader(TensorDataset(X_train, y_train),batch_size=batch_size,shuffle=True
)test_dl = DataLoader(TensorDataset(X_test, y_test),batch_size=batch_size,shuffle=False
)

5. 构建模型

class Rnn_Model(nn.Module):def __init__(self):super().__init__()# 调用rnnself.rnn = nn.RNN(input_size=20, hidden_size=200, num_layers=1, batch_first=True)self.fc1 = nn.Linear(200, 50)self.fc2 = nn.Linear(50, 2)def forward(self, x):x, hidden1 = self.rnn(x)x = self.fc1(x)x = self.fc2(x)return x# 数据不大，cpu即可
device = "cpu"model = Rnn_Model().to(device)
model

Rnn_Model(
(rnn): RNN(20, 200, batch_first=True)
(fc1): Linear(in_features=200, out_features=50, bias=True)
(fc2): Linear(in_features=50, out_features=2, bias=True)
)

model(torch.randn(32, 20)).shape

torch.Size([32, 2])

6. 模型训练

6.1 构建训练函数

def train(data, model, loss_fn, opt):size = len(data.dataset)batch_num = len(data)train_loss, train_acc = 0.0, 0.0for X, y in data:X, y = X.to(device), y.to(device)pred = model(X)loss = loss_fn(pred, y)# 反向传播opt.zero_grad()  # 梯度清零loss.backward()  # 求导opt.step()       # 设置梯度train_loss += loss.item()train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()train_loss /= batch_numtrain_acc /= size return train_acc, train_loss 

6.2 构建测试函数

def test(data, model, loss_fn):size = len(data.dataset)batch_num = len(data)test_loss, test_acc = 0.0, 0.0 with torch.no_grad():for X, y in data: X, y = X.to(device), y.to(device)pred = model(X)loss = loss_fn(pred, y)test_loss += loss.item()test_acc += (pred.argmax(1) == y).type(torch.float).sum().item()test_loss /= batch_numtest_acc /= sizereturn test_acc, test_loss 

6.3 设置超参数

loss_fn = nn.CrossEntropyLoss()  # 损失函数     
learn_lr = 1e-4            # 超参数
optimizer = torch.optim.Adam(model.parameters(), lr=learn_lr) 

7. 模型训练

train_acc = []
train_loss = []
test_acc = []
test_loss = []epoches = 50for i in range(epoches):model.train()epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)model.eval()epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)# 输出template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}')print(template.format(i + 1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss))print("Done")

Epoch: 1, Train_acc:57.9%, Train_loss:0.675, Test_acc:66.0%, Test_loss:0.608
Epoch: 2, Train_acc:67.2%, Train_loss:0.589, Test_acc:68.8%, Test_loss:0.556
Epoch: 3, Train_acc:75.1%, Train_loss:0.540, Test_acc:75.1%, Test_loss:0.506
Epoch: 4, Train_acc:79.1%, Train_loss:0.485, Test_acc:82.1%, Test_loss:0.460
Epoch: 5, Train_acc:83.0%, Train_loss:0.442, Test_acc:81.4%, Test_loss:0.427
Epoch: 6, Train_acc:83.5%, Train_loss:0.411, Test_acc:84.2%, Test_loss:0.407
Epoch: 7, Train_acc:83.3%, Train_loss:0.395, Test_acc:82.8%, Test_loss:0.400
Epoch: 8, Train_acc:84.1%, Train_loss:0.383, Test_acc:84.0%, Test_loss:0.396
Epoch: 9, Train_acc:84.1%, Train_loss:0.380, Test_acc:84.0%, Test_loss:0.394
Epoch:10, Train_acc:83.9%, Train_loss:0.375, Test_acc:84.0%, Test_loss:0.395
Epoch:11, Train_acc:84.5%, Train_loss:0.375, Test_acc:84.4%, Test_loss:0.396
Epoch:12, Train_acc:84.5%, Train_loss:0.374, Test_acc:83.5%, Test_loss:0.399
Epoch:13, Train_acc:83.7%, Train_loss:0.373, Test_acc:83.0%, Test_loss:0.401
Epoch:14, Train_acc:84.3%, Train_loss:0.372, Test_acc:84.0%, Test_loss:0.402
Epoch:15, Train_acc:84.1%, Train_loss:0.375, Test_acc:83.3%, Test_loss:0.400
Epoch:16, Train_acc:84.6%, Train_loss:0.370, Test_acc:83.0%, Test_loss:0.404
Epoch:17, Train_acc:84.2%, Train_loss:0.371, Test_acc:83.0%, Test_loss:0.406
Epoch:18, Train_acc:84.3%, Train_loss:0.377, Test_acc:83.3%, Test_loss:0.401
Epoch:19, Train_acc:84.8%, Train_loss:0.371, Test_acc:83.0%, Test_loss:0.402
Epoch:20, Train_acc:84.8%, Train_loss:0.372, Test_acc:83.5%, Test_loss:0.402
Epoch:21, Train_acc:84.9%, Train_loss:0.374, Test_acc:83.7%, Test_loss:0.399
Epoch:22, Train_acc:85.2%, Train_loss:0.369, Test_acc:83.7%, Test_loss:0.401
Epoch:23, Train_acc:84.7%, Train_loss:0.374, Test_acc:84.4%, Test_loss:0.401
Epoch:24, Train_acc:84.2%, Train_loss:0.371, Test_acc:84.2%, Test_loss:0.398
Epoch:25, Train_acc:84.3%, Train_loss:0.370, Test_acc:83.7%, Test_loss:0.399
Epoch:26, Train_acc:84.8%, Train_loss:0.373, Test_acc:83.7%, Test_loss:0.398
Epoch:27, Train_acc:84.6%, Train_loss:0.373, Test_acc:83.7%, Test_loss:0.395
Epoch:28, Train_acc:85.1%, Train_loss:0.372, Test_acc:83.5%, Test_loss:0.397
Epoch:29, Train_acc:84.4%, Train_loss:0.373, Test_acc:84.4%, Test_loss:0.399
Epoch:30, Train_acc:85.0%, Train_loss:0.371, Test_acc:83.7%, Test_loss:0.401
Epoch:31, Train_acc:84.7%, Train_loss:0.372, Test_acc:83.7%, Test_loss:0.401
Epoch:32, Train_acc:84.5%, Train_loss:0.372, Test_acc:84.0%, Test_loss:0.400
Epoch:33, Train_acc:84.4%, Train_loss:0.369, Test_acc:83.5%, Test_loss:0.397
Epoch:34, Train_acc:84.7%, Train_loss:0.369, Test_acc:83.7%, Test_loss:0.401
Epoch:35, Train_acc:84.6%, Train_loss:0.372, Test_acc:83.3%, Test_loss:0.396
Epoch:36, Train_acc:84.8%, Train_loss:0.370, Test_acc:83.3%, Test_loss:0.396
Epoch:37, Train_acc:84.8%, Train_loss:0.371, Test_acc:83.5%, Test_loss:0.399
Epoch:38, Train_acc:84.9%, Train_loss:0.369, Test_acc:83.5%, Test_loss:0.398
Epoch:39, Train_acc:85.0%, Train_loss:0.370, Test_acc:83.0%, Test_loss:0.395
Epoch:40, Train_acc:83.8%, Train_loss:0.371, Test_acc:83.7%, Test_loss:0.394
Epoch:41, Train_acc:84.6%, Train_loss:0.370, Test_acc:83.7%, Test_loss:0.394
Epoch:42, Train_acc:85.1%, Train_loss:0.370, Test_acc:84.2%, Test_loss:0.392
Epoch:43, Train_acc:84.4%, Train_loss:0.371, Test_acc:84.0%, Test_loss:0.393
Epoch:44, Train_acc:84.5%, Train_loss:0.372, Test_acc:84.7%, Test_loss:0.396
Epoch:45, Train_acc:85.3%, Train_loss:0.372, Test_acc:84.2%, Test_loss:0.396
Epoch:46, Train_acc:85.0%, Train_loss:0.368, Test_acc:84.4%, Test_loss:0.397
Epoch:47, Train_acc:85.0%, Train_loss:0.372, Test_acc:84.0%, Test_loss:0.395
Epoch:48, Train_acc:84.5%, Train_loss:0.370, Test_acc:84.4%, Test_loss:0.394
Epoch:49, Train_acc:85.1%, Train_loss:0.368, Test_acc:84.2%, Test_loss:0.400
Epoch:50, Train_acc:84.9%, Train_loss:0.370, Test_acc:84.2%, Test_loss:0.397
Done

8. 模型评估

8.1 结果图

import matplotlib.pyplot as plt
#隐藏警告
import warnings
warnings.filterwarnings("ignore")               #忽略警告信息
from datetime import datetime
current_time = datetime.now() # 获取当前时间epochs_range = range(epoches)plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training Accuracy')
plt.xlabel(current_time) # 打卡请带上时间戳，否则代码截图无效plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training= Loss')
plt.show()

8.2 混淆矩阵

from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay pred = model(X_test.to(device)).argmax(1).cpu().numpy()# 计算混淆矩阵
cm = confusion_matrix(y_test, pred)# 计算
plt.figure(figsize=(6, 5))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues")
# 标题
plt.title("混淆矩阵")
plt.xlabel("Predicted Label")
plt.ylabel("True Label")plt.tight_layout()  # 自适应
plt.show()

9. 总结：

本周在上周的基础上更加完善了阿尔茨海默病诊断模型，加入了REF（递归特征消除）特征选择方法。并且通过实践更好的理解了模型以及该如何使用这种特征选择方法。