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为了演示临床因素的分析#xff0c;让我们模拟一个数据集并执行一些基本的统计和机器学习分析。我们将重点关注以下步骤#xff1a;
模拟数据集#xff1a;创建具有年龄、性别、BMI、吸烟状况和疾病结果等特征的临床数据。描述性统计#xff1a;…Python片段
为了演示临床因素的分析让我们模拟一个数据集并执行一些基本的统计和机器学习分析。我们将重点关注以下步骤
模拟数据集创建具有年龄、性别、BMI、吸烟状况和疾病结果等特征的临床数据。描述性统计使用平均值、标准差和分布总结数据。相关性分析识别临床因素之间的关系。预测模型建立逻辑回归模型根据临床特征预测疾病结果。可视化绘制重要关系和模型性能。
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report# Step 1: Simulate the dataset
np.random.seed(42)# Simulating clinical factors
n_samples 500
age np.random.normal(50, 12, n_samples).clip(18, 90) # Age between 18 and 90
gender np.random.choice([Male, Female], n_samples) # Binary gender
bmi np.random.normal(25, 5, n_samples).clip(15, 50) # BMI between 15 and 50
smoking_status np.random.choice([Smoker, Non-Smoker], n_samples, p[0.3, 0.7])
disease_outcome np.random.choice([0, 1], n_samples, p[0.7, 0.3]) # Disease prevalence of 30%# Combine into a DataFrame
data pd.DataFrame({Age: age,Gender: gender,BMI: bmi,Smoking_Status: smoking_status,Disease_Outcome: disease_outcome
})# Encode categorical variables
data[Gender] data[Gender].map({Male: 1, Female: 0})
data[Smoking_Status] data[Smoking_Status].map({Smoker: 1, Non-Smoker: 0})data.head()ResultAge Gender BMI Smoking_Status Disease_Outcome
0 55.960570 0 21.478282 0 0
1 48.340828 0 17.957694 0 0
2 57.772262 0 17.216854 0 0
3 68.276358 1 28.030050 0 1
4 47.190160 0 18.597853 1 1模拟数据集包含 500 个样本包含以下列
年龄表示患者年龄的连续变量。性别二元变量1 男性0 女性。BMI表示体重指数的连续变量。吸烟状况二元变量1 吸烟者0 非吸烟者。疾病结果二元结果变量1 有病0 无病。
下一步
描述性统计和可视化相关性分析预测模型
让我们计算描述性统计并可视化数据分布
# Descriptive statistics
desc_stats data.describe()# Visualizations
plt.figure(figsize(14, 6))
plt.subplot(1, 3, 1)
sns.histplot(data[Age], kdeTrue, bins20, colorblue)
plt.title(Age Distribution)plt.subplot(1, 3, 2)
sns.histplot(data[BMI], kdeTrue, bins20, colorgreen)
plt.title(BMI Distribution)plt.subplot(1, 3, 3)
sns.countplot(xSmoking_Status, datadata, palettepastel)
plt.title(Smoking Status)plt.tight_layout()
plt.show()desc_statsResultAge Gender BMI Smoking_Status Disease_Outcome
count 500.000000 500.0000 500.000000 500.000000 500.000000
mean 50.083381 0.5200 25.540743 0.306000 0.298000
std 11.687369 0.5001 4.864940 0.461291 0.457838
min 18.000000 0.0000 15.000000 0.000000 0.000000
25% 41.596311 0.0000 22.200960 0.000000 0.000000
50% 50.153566 1.0000 25.652685 0.000000 0.000000
75% 57.641399 1.0000 28.568301 1.000000 1.000000
max 90.000000 1.0000 38.161910 1.000000 1.000000描述性统计
年龄平均年龄约为 50 岁标准差约为 11.7 岁。范围为 18-90 岁。BMI平均年龄约为 25.5与正常 BMI 范围一致标准差约为 4.9。吸烟状况约 30.6% 的人口是吸烟者。疾病结果约 29.8% 患有该疾病与模拟患病率一致。
可视化观察结果
年龄分布以平均年龄~50为中心的对称分布。BMI 分布以~25 为中心的正态分布。吸烟状况非吸烟者占主导地位~70% 的样本。
接下来我们分析这些因素与疾病结果之间的相关性。
# Correlation matrix (including only numerical variables)
correlation_matrix data.corr()# Plotting the correlation heatmap
plt.figure(figsize(8, 6))
sns.heatmap(correlation_matrix, annotTrue, cmapcoolwarm, fmt.2f)
plt.title(Correlation Matrix)
plt.show()correlation_matrixResultAge Gender BMI Smoking_Status Disease_Outcome
Age 1.000000 0.062456 -0.005557 -0.064929 -0.065476
Gender 0.062456 1.000000 -0.071286 0.029883 0.074571
BMI -0.005557 -0.071286 1.000000 -0.034530 -0.087904
Smoking_Status -0.064929 0.029883 -0.034530 1.000000 0.013341
Disease_Outcome -0.065476 0.074571 -0.087904 0.013341 1.00000相关性分析 疾病结果 与性别呈弱正相关性~0.075表明男性患病率可能略高。与年龄-0.065和 BMI-0.088呈非常弱的负相关性表明没有很强的线性关系。 其他特征 其他特征之间的相关性非常小表明多重共线性较低
我们将建立一个逻辑回归模型根据临床特征预测疾病结果。这将深入了解有助于疾病预测的因素。
# Splitting the dataset into training and testing sets
X data[[Age, Gender, BMI, Smoking_Status]]
y data[Disease_Outcome]
X_train, X_test, y_train, y_test train_test_split(X, y, test_size0.3, random_state42)# Logistic regression model
log_reg LogisticRegression()
log_reg.fit(X_train, y_train)# Predictions and evaluation
y_pred log_reg.predict(X_test)
accuracy accuracy_score(y_test, y_pred)
conf_matrix confusion_matrix(y_test, y_pred)
class_report classification_report(y_test, y_pred)accuracy, conf_matrix, class_report
MATLAB片段
% Simulate Clinical Dataset
rng(42); % For reproducibility
numPatients 500;% Generate clinical factors
Age randi([30, 80], numPatients, 1); % Age in years
BMI 18 15*rand(numPatients, 1); % BMI
BP randi([90, 160], numPatients, 1); % Blood Pressure
Cholesterol randi([150, 300], numPatients, 1); % Cholesterol levels
Smoking randi([0, 1], numPatients, 1); % Smoking status (0No, 1Yes)% Generate binary outcome: Heart Disease (1) or No Heart Disease (0)
% Using a logistic model for simulation
coeff [0.03, 0.04, 0.05, 0.02, 0.5]; % Coefficients for logistic function
X [Age, BMI, BP, Cholesterol, Smoking];
logit_prob 1 ./ (1 exp(-(X * coeff - 10))); % Logistic probability
HeartDisease double(rand(numPatients, 1) logit_prob); % Generate outcome% Combine into a table
clinicalData table(Age, BMI, BP, Cholesterol, Smoking, HeartDisease);% Data Preprocessing: Normalize numeric variables
numericVars {Age, BMI, BP, Cholesterol};
for i 1:length(numericVars)clinicalData.(numericVars{i}) (clinicalData.(numericVars{i}) - ...mean(clinicalData.(numericVars{i}))) / std(clinicalData.(numericVars{i}));
end% Statistical Analysis
disp(Correlation Matrix:);
corrMatrix corr(table2array(clinicalData(:, 1:5))); % Correlation of predictors
disp(corrMatrix);% Logistic Regression Model
X table2array(clinicalData(:, 1:5)); % Features
y clinicalData.HeartDisease; % Outcome
mdl fitglm(X, y, Distribution, binomial, Link, logit);% Display Model Coefficients
disp(Model Coefficients:);
disp(mdl.Coefficients);% Evaluate Model: Predict on the dataset
predictedProb predict(mdl, X);
predictedOutcome predictedProb 0.5;% Confusion Matrix and Accuracy
confMat confusionmat(y, predictedOutcome);
disp(Confusion Matrix:);
disp(confMat);accuracy sum(diag(confMat)) / sum(confMat(:));
disp([Accuracy: , num2str(accuracy)]);% Plot Receiver Operating Characteristic (ROC) Curve
[Xroc, Yroc, T, AUC] perfcurve(y, predictedProb, 1);
disp([AUC: , num2str(AUC)]);figure;
plot(Xroc, Yroc);
xlabel(False Positive Rate);
ylabel(True Positive Rate);
title(ROC Curve);
grid on;更新亚图跨际
