第R9周：阿尔茨海默病诊断（优化特征选择版）
文章目录
- 1. 导入数据
- 2. 数据处理
- 2.1 患病占比
- 2.2 相关性分析
- 2.3 年龄与患病探究
- 3. 特征选择
- 4. 构建数据集
- 4.1 数据集划分与标准化
- 4.2 构建加载
- 5. 构建模型
- 6. 模型训练
- 6.1 构建训练函数
- 6.2 构建测试函数
- 6.3 设置超参数
- 7. 模型训练
- 8. 模型评估
- 8.1 结果图
- 8.2 混淆矩阵
- 9. 总结：
- 🍨 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。