In the realm of healthcare analytics, the Logistic Regression for Predicting Heart Disease Risk project focuses on using logistic regression to predict the Ten-Year Coronary Heart Disease (CHD) risk based on various health-related features. By analyzing a dataset encompassing demographic and health parameters, this project aims to enhance early detection and management strategies for CHD.
- Implements logistic regression for binary classification of Ten-Year CHD risk.
- Utilizes a dataset containing features such as age, sex, smoking habits, cholesterol levels, blood pressure, and glucose levels.
- Includes data preprocessing steps including data cleaning, normalization, and splitting into training and testing sets.
- Evaluates model performance using accuracy metrics to gauge predictive efficacy.
To start, import essential Python modules required for data analysis and machine learning, including pandas, numpy, scikit-learn, matplotlib, and seaborn. The dataset used in this project is loaded using pandas' read_csv function, ensuring data accessibility and management.
Perform initial data preprocessing steps to prepare the dataset for analysis. This includes handling missing values, renaming columns for clarity, and normalizing numerical features using sklearn's StandardScaler.
Train a logistic regression model using scikit-learn's LogisticRegression class, evaluate its performance on the test set using accuracy metrics, and visualize results using seaborn for clarity.
This project demonstrates the application of logistic regression in predicting Ten-Year CHD risk based on demographic and health data. The achieved accuracy of 0.84 showcases the model's effectiveness in assisting healthcare professionals with early risk assessment and intervention strategies.