深度学习项目--基于LSTM的糖尿病预测探究(pytorch实现)

羊小猪~~2025-01-27 10:40

前言

文章目录

1、数据导入和数据预处理

1、数据导入

python 复制代码 
import torch  
import torch.nn as nn 
from torch.utils.data import DataLoader, TensorDataset
import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
import seaborn as sns 
from sklearn.preprocessing import StandardScaler 
from sklearn.model_selection import train_test_split
#设置字体
from pylab import mpl
mpl.rcParams["font.sans-serif"] = ["SimHei"]  # 显示中文
plt.rcParams['axes.unicode_minus'] = False		# 显示负号

# 数据不大，用CPU即可
device = 'cpu'

data_df = pd.read_excel('./dia.xls')

data_df.head()

| | 卡号 | 性别 | 年龄 | 高密度脂蛋白胆固醇 | 低密度脂蛋白胆固醇 | 极低密度脂蛋白胆固醇 | 甘油三酯 | 总胆固醇 | 脉搏 | 舒张压 | 高血压史 | 尿素氮 | 尿酸 | 肌酐 | 体重检查结果 | 是否糖尿病 |
| 0 | 18054421 | 0 | 38 | 1.25 | 2.99 | 1.07 | 0.64 | 5.31 | 83 | 83 | 0 | 4.99 | 243.3 | 50 | 1 | 0 |
| 1 | 18054422 | 0 | 31 | 1.15 | 1.99 | 0.84 | 0.50 | 3.98 | 85 | 63 | 0 | 4.72 | 391.0 | 47 | 1 | 0 |
| 2 | 18054423 | 0 | 27 | 1.29 | 2.21 | 0.69 | 0.60 | 4.19 | 73 | 61 | 0 | 5.87 | 325.7 | 51 | 1 | 0 |
| 3 | 18054424 | 0 | 33 | 0.93 | 2.01 | 0.66 | 0.84 | 3.60 | 83 | 60 | 0 | 2.40 | 203.2 | 40 | 2 | 0 |

4 18054425 0 36 1.17 2.83 0.83 0.73 4.83 85 67 0 4.09 236.8 43 0 0

2、数据统计

python 复制代码 
data_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1006 entries, 0 to 1005
Data columns (total 16 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   卡号          1006 non-null   int64  
 1   性别          1006 non-null   int64  
 2   年龄          1006 non-null   int64  
 3   高密度脂蛋白胆固醇   1006 non-null   float64
 4   低密度脂蛋白胆固醇   1006 non-null   float64
 5   极低密度脂蛋白胆固醇  1006 non-null   float64
 6   甘油三酯        1006 non-null   float64
 7   总胆固醇        1006 non-null   float64
 8   脉搏          1006 non-null   int64  
 9   舒张压         1006 non-null   int64  
 10  高血压史        1006 non-null   int64  
 11  尿素氮         1006 non-null   float64
 12  尿酸          1006 non-null   float64
 13  肌酐          1006 non-null   int64  
 14  体重检查结果      1006 non-null   int64  
 15  是否糖尿病       1006 non-null   int64  
dtypes: float64(7), int64(9)
memory usage: 125.9 KB
python 复制代码 
data_df.describe()

| | 卡号 | 性别 | 年龄 | 高密度脂蛋白胆固醇 | 低密度脂蛋白胆固醇 | 极低密度脂蛋白胆固醇 | 甘油三酯 | 总胆固醇 | 脉搏 | 舒张压 | 高血压史 | 尿素氮 | 尿酸 | 肌酐 | 体重检查结果 | 是否糖尿病 |
| count | 1.006000e+03 | 1006.000000 | 1006.000000 | 1006.000000 | 1006.000000 | 1006.000000 | 1006.000000 | 1006.000000 | 1006.000000 | 1006.000000 | 1006.000000 | 1006.000000 | 1006.000000 | 1006.000000 | 1006.000000 | 1006.000000 |
| mean | 1.838279e+07 | 0.598410 | 50.288270 | 1.152201 | 2.707475 | 0.998311 | 1.896720 | 4.857624 | 80.819085 | 76.886680 | 0.173956 | 5.562684 | 339.345427 | 64.106362 | 1.609344 | 0.444334 |
| std | 6.745088e+05 | 0.490464 | 16.921487 | 0.313426 | 0.848070 | 0.715891 | 2.421403 | 1.029973 | 12.542270 | 12.763173 | 0.379260 | 1.646342 | 84.569846 | 29.338437 | 0.772327 | 0.497139 |
| min | 1.805442e+07 | 0.000000 | 20.000000 | 0.420000 | 0.840000 | 0.140000 | 0.350000 | 2.410000 | 41.000000 | 45.000000 | 0.000000 | 2.210000 | 140.800000 | 30.000000 | 0.000000 | 0.000000 |
| 25% | 1.807007e+07 | 0.000000 | 37.250000 | 0.920000 | 2.100000 | 0.680000 | 0.880000 | 4.200000 | 72.000000 | 67.000000 | 0.000000 | 4.450000 | 280.850000 | 51.250000 | 1.000000 | 0.000000 |
| 50% | 1.807036e+07 | 1.000000 | 50.000000 | 1.120000 | 2.680000 | 0.850000 | 1.335000 | 4.785000 | 79.000000 | 76.000000 | 0.000000 | 5.340000 | 333.000000 | 62.000000 | 2.000000 | 0.000000 |
| 75% | 1.809726e+07 | 1.000000 | 60.000000 | 1.320000 | 3.220000 | 1.090000 | 2.087500 | 5.380000 | 88.000000 | 85.000000 | 0.000000 | 6.367500 | 394.000000 | 72.000000 | 2.000000 | 1.000000 |

max 2.026124e+07 1.000000 93.000000 2.500000 7.980000 11.260000 45.840000 12.610000 135.000000 119.000000 1.000000 18.640000 679.000000 799.000000 3.000000 1.000000

3、数据分布分析

python 复制代码 
# 缺失值统计
data_df.isnull().sum()
卡号            0
性别            0
年龄            0
高密度脂蛋白胆固醇     0
低密度脂蛋白胆固醇     0
极低密度脂蛋白胆固醇    0
甘油三酯          0
总胆固醇          0
脉搏            0
舒张压           0
高血压史          0
尿素氮           0
尿酸            0
肌酐            0
体重检查结果        0
是否糖尿病         0
dtype: int64
python 复制代码 
# 数据分布、异常值分析
feature_name = {
    '性别': '性别',
    '年龄': '年龄',
    '高密度脂蛋白胆固醇': '高密度脂蛋白胆固醇',
    '低密度脂蛋白胆固醇': '低密度脂蛋白胆固醇',
    '极低密度脂蛋白胆固醇': '极低密度脂蛋白胆固醇',
    '甘油三酯': '甘油三酯',
    '总胆固醇': '总胆固醇',
    '脉搏': '脉搏',
    '舒张压': '舒张压',
    '高血压史': '高血压史',
    '尿素氮': '尿素氮',
    '肌酐': '肌酐',
    '体重检查结果': '体重检查结果',
    '是否糖尿病': '是否糖尿病'
}

# 子箱图 展示
plt.figure(figsize=(20, 20))

for i, (col, col_name) in enumerate(feature_name.items(), 1):
    plt.subplot(4, 4, i)
    # 绘制子箱图
    sns.boxplot(x=data_df["是否糖尿病"],y=data_df[col])
    # 设置标题
    plt.title(f'{col_name}的纸箱图', fontsize=10)
    plt.ylabel('数值', fontsize=12)
    plt.grid(axis='y', linestyle='--', alpha=0.7)
    
plt.show()

​

​

异常值分析(查阅资料后发现)：

  1. 总数据较少；
  2. 特征参数受很多因素的影响，故这里假设没有异常值(数据多的时候可以进一步分析)

患糖尿病和不患糖尿病数据分布分析：

  • 发现患病和不患病在：年龄、高密度蛋白固醇、低密度蛋白固醇、低密度蛋白固醇、甘油三肪、舒张压、高血压、尿素的相关因素等数据因素有关

4、相关性分析

python 复制代码 
plt.figure(figsize=(15, 10))
sns.heatmap(data_df.corr(), annot=True, fmt=".2f")
plt.show()

​

​

高密度蛋白胆固醇存在负相关，故删除该特征

2、数据标准化和划分

时间步长为1

python 复制代码 
# 特征选择
x = data_df.drop(['卡号', '高密度脂蛋白胆固醇', '是否糖尿病'], axis=1)
y = data_df['是否糖尿病']

# 数据标准化(数据之间差别大), 二分类问题，y不需要做标准化
sc = StandardScaler()
x = sc.fit_transform(x)

# 转换为tensors数据
x = torch.tensor(np.array(x), dtype=torch.float32)
y = torch.tensor(np.array(y), dtype=torch.int64)

# 数据划分, 训练：测试 = 8: 2
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2 ,random_state=42)

# 维度设置, [batch_size, seq, features], 当然不设置也没事，因为这样默认** 设置 seq 为 1**
x_train = x_train.unsqueeze(1)
x_test = x_test.unsqueeze(1)

# 查看维度
x_train.shape, y_train.shape
(torch.Size([804, 1, 13]), torch.Size([804]))
python 复制代码 
# 构建数据集
batch_size = 16

train_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
                     )
python 复制代码 
for X, Y in train_dl:
    print(X.shape)
    print(Y.shape)
    break 
torch.Size([16, 1, 13])
torch.Size([16])

3、创建模型

python 复制代码 
class Model_lstm(nn.Module):
    def __init__(self):
        super().__init__()
        
        '''
        模型结构:
            1、两层lstm
            2、一层linear 
        '''
        
        self.lstm1 = nn.LSTM(input_size=13, hidden_size=200,
                             num_layers=1, batch_first=True)
        self.lstm2 = nn.LSTM(input_size=200, hidden_size=200,
                             num_layers=1, batch_first=True)
        # 展开，分类
        self.lc1 = nn.Linear(200, 2)
        
    def forward(self, x):
        out, hidden1 = self.lstm1(x)
        out, _ = self.lstm2(out, hidden1)  # 将上一个层的最后隐藏层状态，作为lstm2的这一层的隐藏层状态
        out = self.lc1(out)
        return out
    
model = Model_lstm().to(device)

model
Model_lstm(
  (lstm1): LSTM(13, 200, batch_first=True)
  (lstm2): LSTM(200, 200, batch_first=True)
  (lc1): Linear(in_features=200, out_features=2, bias=True)
)
python 复制代码 
model(torch.randn(8, 1, 13)).shape
torch.Size([8, 1, 2])

4、模型训练

1、创建训练集

python 复制代码 
def train(dataloader, model, loss_fn, opt):
    size = len(dataloader.dataset)
    num_batch = len(dataloader)
    
    train_acc, train_loss = 0.0, 0.0 
    
    for X, y in dataloader:
        X, y = X.to(device), y.to(device)
        
        pred = model(X).view(-1, 2)
        loss = loss_fn(pred, y)
        
        # 梯度设置
        opt.zero_grad()
        loss.backward()
        opt.step()
        
        train_loss += loss.item()
        # 求最大概率配对
        train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
        
    train_acc /= size 
    train_loss /= num_batch
    
    return train_acc, train_loss

2、创建测试集函数

python 复制代码 
def test(dataloader, model, loss_fn):
    size = len(dataloader.dataset)
    num_batch = len(dataloader)
    
    test_acc, test_loss = 0.0, 0.0 
    
    with torch.no_grad():
        for X, y in dataloader:
            X, y = X.to(device), y.to(device)
            
            pred = model(X).view(-1, 2)
            loss = loss_fn(pred, y)
            
            test_loss += loss.item()
            # 求最大概率配对
            test_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
            
    test_acc /= size 
    test_loss /= num_batch 
    
    return test_acc, test_loss

3、设置超参数

python 复制代码 
learn_rate = 1e-4
opt = torch.optim.Adam(model.parameters(), lr=learn_rate)
loss_fn = nn.CrossEntropyLoss()

5、模型训练

python 复制代码 
epochs = 50

train_acc, train_loss, test_acc, test_loss = [], [], [], []

for i in range(epochs):
    model.train()
    epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)
    
    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:58.5%, Train_loss:0.677, Test_acc:75.7%, Test_loss:0.655
Epoch: 2, Train_acc:71.0%, Train_loss:0.643, Test_acc:77.2%, Test_loss:0.606
Epoch: 3, Train_acc:75.2%, Train_loss:0.590, Test_acc:79.7%, Test_loss:0.533
Epoch: 4, Train_acc:76.9%, Train_loss:0.524, Test_acc:80.2%, Test_loss:0.469
Epoch: 5, Train_acc:77.5%, Train_loss:0.481, Test_acc:79.7%, Test_loss:0.436
Epoch: 6, Train_acc:78.4%, Train_loss:0.470, Test_acc:79.7%, Test_loss:0.419
Epoch: 7, Train_acc:78.6%, Train_loss:0.452, Test_acc:80.7%, Test_loss:0.412
Epoch: 8, Train_acc:78.5%, Train_loss:0.449, Test_acc:80.7%, Test_loss:0.406
Epoch: 9, Train_acc:78.7%, Train_loss:0.444, Test_acc:80.7%, Test_loss:0.400
Epoch:10, Train_acc:79.0%, Train_loss:0.435, Test_acc:81.2%, Test_loss:0.395
Epoch:11, Train_acc:78.4%, Train_loss:0.428, Test_acc:81.2%, Test_loss:0.391
Epoch:12, Train_acc:79.1%, Train_loss:0.428, Test_acc:81.2%, Test_loss:0.388
Epoch:13, Train_acc:79.0%, Train_loss:0.421, Test_acc:80.7%, Test_loss:0.385
Epoch:14, Train_acc:79.2%, Train_loss:0.415, Test_acc:81.7%, Test_loss:0.382
Epoch:15, Train_acc:79.1%, Train_loss:0.415, Test_acc:81.7%, Test_loss:0.379
Epoch:16, Train_acc:79.7%, Train_loss:0.422, Test_acc:81.7%, Test_loss:0.377
Epoch:17, Train_acc:79.5%, Train_loss:0.410, Test_acc:81.7%, Test_loss:0.375
Epoch:18, Train_acc:79.2%, Train_loss:0.406, Test_acc:81.7%, Test_loss:0.374
Epoch:19, Train_acc:80.3%, Train_loss:0.407, Test_acc:82.2%, Test_loss:0.372
Epoch:20, Train_acc:80.1%, Train_loss:0.409, Test_acc:81.2%, Test_loss:0.370
Epoch:21, Train_acc:80.2%, Train_loss:0.397, Test_acc:80.7%, Test_loss:0.368
Epoch:22, Train_acc:81.0%, Train_loss:0.399, Test_acc:81.7%, Test_loss:0.367
Epoch:23, Train_acc:80.7%, Train_loss:0.396, Test_acc:81.2%, Test_loss:0.365
Epoch:24, Train_acc:81.0%, Train_loss:0.401, Test_acc:81.7%, Test_loss:0.363
Epoch:25, Train_acc:81.1%, Train_loss:0.392, Test_acc:82.2%, Test_loss:0.363
Epoch:26, Train_acc:81.2%, Train_loss:0.385, Test_acc:82.2%, Test_loss:0.362
Epoch:27, Train_acc:80.6%, Train_loss:0.392, Test_acc:82.2%, Test_loss:0.361
Epoch:28, Train_acc:80.5%, Train_loss:0.382, Test_acc:81.2%, Test_loss:0.358
Epoch:29, Train_acc:81.1%, Train_loss:0.386, Test_acc:81.7%, Test_loss:0.358
Epoch:30, Train_acc:80.7%, Train_loss:0.380, Test_acc:82.2%, Test_loss:0.358
Epoch:31, Train_acc:81.5%, Train_loss:0.378, Test_acc:81.7%, Test_loss:0.357
Epoch:32, Train_acc:80.6%, Train_loss:0.373, Test_acc:81.2%, Test_loss:0.356
Epoch:33, Train_acc:81.3%, Train_loss:0.373, Test_acc:81.7%, Test_loss:0.357
Epoch:34, Train_acc:80.8%, Train_loss:0.378, Test_acc:81.7%, Test_loss:0.354
Epoch:35, Train_acc:81.5%, Train_loss:0.372, Test_acc:81.2%, Test_loss:0.355
Epoch:36, Train_acc:81.5%, Train_loss:0.368, Test_acc:81.2%, Test_loss:0.354
Epoch:37, Train_acc:81.2%, Train_loss:0.368, Test_acc:80.7%, Test_loss:0.354
Epoch:38, Train_acc:81.2%, Train_loss:0.369, Test_acc:81.2%, Test_loss:0.353
Epoch:39, Train_acc:81.7%, Train_loss:0.365, Test_acc:81.2%, Test_loss:0.354
Epoch:40, Train_acc:81.5%, Train_loss:0.363, Test_acc:81.2%, Test_loss:0.355
Epoch:41, Train_acc:81.7%, Train_loss:0.358, Test_acc:81.2%, Test_loss:0.354
Epoch:42, Train_acc:81.7%, Train_loss:0.355, Test_acc:81.2%, Test_loss:0.353
Epoch:43, Train_acc:81.3%, Train_loss:0.353, Test_acc:80.7%, Test_loss:0.354
Epoch:44, Train_acc:82.0%, Train_loss:0.355, Test_acc:80.7%, Test_loss:0.354
Epoch:45, Train_acc:81.7%, Train_loss:0.353, Test_acc:79.7%, Test_loss:0.354
Epoch:46, Train_acc:82.1%, Train_loss:0.354, Test_acc:80.2%, Test_loss:0.354
Epoch:47, Train_acc:82.0%, Train_loss:0.349, Test_acc:80.2%, Test_loss:0.356
Epoch:48, Train_acc:82.1%, Train_loss:0.350, Test_acc:80.2%, Test_loss:0.356
Epoch:49, Train_acc:82.0%, Train_loss:0.345, Test_acc:80.7%, Test_loss:0.355
Epoch:50, Train_acc:81.8%, Train_loss:0.344, Test_acc:80.7%, Test_loss:0.355
---------------Done---------------

6、模型结果展示

python 复制代码 
from datetime import datetime
current_time = datetime.now()

epochs_range = range(epochs)

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) 

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()

​

7、预测

python 复制代码 
test_x = x_test[0].reshape(1, 1, 13)

pred = model(test_x.to(device)).reshape(-1, 2)
res = pred.argmax(1).item()

print(f"预测结果: {res}, (1: 患病; 0: 不患病)")
预测结果: 1, (1: 患病; 0: 不患病)
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