r6：LSTM实现糖尿病探索与预测-程序员充电站

- **🍨 本文为[🔗365天深度学习训练营](https://mp.weixin.qq.com/s/o-DaK6aQQLkJ8uE4YX1p3Q) 中的学习记录博客**
- **🍖 原作者：[K同学啊](https://mtyjkh.blog.csdn.net/)**

文章目录

- 概要
- 整体架构流程
- 代码运行
- 技术名词解释
- 小结

概要

这是一个用LSTM 神经网络预测糖尿病的项目。

整体架构流程

读取数据 → 数据可视化 → 数据预处理 → 构建模型 → 训练模型 → 评估结果

读取数据— 从 Excel 读入1006条患者体检数据
可视化— 用箱线图看各指标与糖尿病的关系
预处理— 去掉无关列，转成 Tensor，切分训练集/测试集（8:2）
构建模型— 两层 LSTM + 一层全连接，输出0或1（是否糖尿病）
训练— 跑30轮，每轮记录准确率和损失值
评估— 画出 Loss 和 Accuracy 曲线，观察模型效果

代码运行

import torch import torch.nn as nn import torch.nn.functional as F import torchvision # M2 芯片设置 device = torch.device("mps" if torch.backends.mps.is_available() else "cpu") print("使用设备:", device) import numpy as np #数字计算 import pandas as pd #处理表格数据 import seaborn as sns #画统计图 from sklearn.model_selection import train_test_split #划分训练集和测试集 import matplotlib.pyplot as plt #画图 plt.rcParams['savefig.dpi'] = 500 # 保存图片时的像素，500很清晰 plt.rcParams['figure.dpi'] = 500 # 显示图片时的分辨率 plt.rcParams['font.sans-serif'] = ['SimHei'] # 支持显示中文，否则中文会变成方块 import warnings warnings.filterwarnings("ignore") # 忽略警告信息 DataFrame = pd.read_excel('/Users/lilyj/Downloads/dia.xls') # 读取一个叫 dia.xls 的 Excel 文件 DataFrame.head() # 显示前5行，看看数据长什么样

print('数据缺失值-----------------------------') print(DataFrame.isnull().sum()) # 每列缺失值的数量

plt.rcParams['font.sans-serif'] = ['Arial Unicode MS'] plt.rcParams['axes.unicode_minus'] = False feature_map = { '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', '尿酸': '尿酸', '肌酐': '肌酐', '体重检查结果': '体重检查结果' # 修正这里 } plt.figure(figsize=(15, 10)) # 宽15，高10（单位：英寸） for i, (col, col_name) in enumerate(feature_map.items(), 1): plt.subplot(3, 5, i) sns.boxplot(x=DataFrame['是否糖尿病'], y=DataFrame[col]) plt.title(f'{col_name}的箱线图', fontsize=14) plt.ylabel('数值', fontsize=12) plt.grid(axis='y', linestyle='--', alpha=0.7) plt.tight_layout() plt.show()

from sklearn.preprocessing import StandardScaler # '高密度脂蛋白胆固醇'与糖尿病负相关，故在X中去掉该字段 X = DataFrame.drop(['卡号','是否糖尿病','高密度脂蛋白胆固醇'], axis=1) y = DataFrame['是否糖尿病'] # sc_X = StandardScaler() # X = sc_X.fit_transform(X) X = torch.tensor(np.array(X), dtype=torch.float32) y = torch.tensor(np.array(y), dtype=torch.int64) train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.2, random_state=1) train_X.shape, train_y.shape from torch.utils.data import TensorDataset, DataLoader train_dl = DataLoader(TensorDataset(train_X, train_y), batch_size=64, shuffle=False) test_dl = DataLoader(TensorDataset(test_X, test_y), batch_size=64, shuffle=False) class model_lstm(nn.Module): def __init__(self): super(model_lstm, self).__init__() self.lstm0 = nn.LSTM(input_size=13, hidden_size=200, num_layers=1, batch_first=True) self.lstm1 = nn.LSTM(input_size=200, hidden_size=200, num_layers=1, batch_first=True) self.fc0 = nn.Linear(200, 2) def forward(self, x): out, hidden1 = self.lstm0(x) out, _ = self.lstm1(out, hidden1) out = self.fc0(out) return out model = model_lstm().to(device) model

# 训练循环 def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) # 训练集的大小 num_batches = len(dataloader) # 批次数目（size/batch_size，向上取整） train_loss, train_acc = 0, 0 # 初始化训练损失和正确率 for X, y in dataloader: # 获取图片及其标签 X, y = X.to(device), y.to(device) # 计算预测误差 pred = model(X) # 网络输出 loss = loss_fn(pred, y) # 计算网络输出和真实值之间的差距 # 反向传播 optimizer.zero_grad() # grad属性归零 loss.backward() # 反向传播 optimizer.step() # 每一步自动更新 # 记录acc与loss train_acc += (pred.argmax(1) == y).type(torch.float).sum().item() train_loss += loss.item() train_acc /= size train_loss /= num_batches return train_acc, train_loss def test(dataloader, model, loss_fn): size = len(dataloader.dataset) # 测试集的大小 num_batches = len(dataloader) # 批次数目 test_loss, test_acc = 0, 0 # 当不进行训练时，停止梯度更新，节省计算内存消耗 with torch.no_grad(): for imgs, target in dataloader: imgs, target = imgs.to(device), target.to(device) # 计算loss target_pred = model(imgs) loss = loss_fn(target_pred, target) test_loss += loss.item() test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item() test_acc /= size test_loss /= num_batches return test_acc, test_loss loss_fn = nn.CrossEntropyLoss() # 创建损失函数 learn_rate = 1e-4 # 学习率 opt = torch.optim.Adam(model.parameters(), lr=learn_rate) epochs = 30 train_loss = [] train_acc = [] test_loss = [] test_acc = [] for epoch 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) # 获取当前的学习率 lr = opt.state_dict()['param_groups'][0]['lr'] template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E}') print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss, lr)) print("="*20, 'Done', "="*20)

import matplotlib.pyplot as plt import warnings warnings.filterwarnings("ignore") # 忽略警告信息 plt.rcParams['font.sans-serif'] = ['Arial Unicode MS'] # 改成Mac支持的字体 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 plt.rcParams['figure.dpi'] = 100 # 分辨率 from datetime import datetime current_time = datetime.now() # 获取当前时间 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 and Validation Accuracy') plt.xlabel(current_time) # 打卡请带上时间截图，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 and Validation Loss') plt.show()