This project aims to predict heart failure events using clinical records sourced from Kaggle. Leveraging machine learning algorithms, I analyze a dataset of heart failure patients to build a predictive model. Among the various algorithms tested, the Decision Tree algorithm provided the best performance. This model helps in identifying patients at risk of heart failure, which can be crucial for early intervention and treatment.
- Data Analysis and Visualization: Understand the data through comprehensive analysis and visualizations.
- Feature Selection: Identify important features influencing heart failure.
- Machine Learning Models: Build and evaluate multiple predictive models, including Decision Tree, Random Forest, and others.
- Performance Evaluation: Measure the accuracy and effectiveness of the models.
To run this project, you need the following Python libraries:
pandasnumpymatplotlibseabornscikit-learn
You can install these dependencies using:
pip install pandas numpy matplotlib seaborn scikit-learnThe dataset used in this project is sourced from Kaggle and consists of clinical records of patients with heart failure. It includes various attributes such as age, gender, blood pressure, ejection fraction, serum creatinine, and others, along with the target variable indicating whether the patient experienced a heart failure event.
I start by importing the necessary libraries and loading the dataset from a CSV file.
I perform an initial exploration of the data to understand its structure and check for missing values.
I analyze the correlation between different features to identify relationships.
I select relevant features and split the data into training and testing sets.
I build multiple machine learning models, including Decision Tree and Random Forest, and evaluate their accuracy. The Decision Tree algorithm provided the best performance in this case.
The Decision Tree model provides the highest prediction accuracy among the models tested. With further tuning and feature engineering, the accuracy can be improved even further.
This project demonstrates a practical application of machine learning in healthcare, specifically in predicting heart failure events. The model developed can assist healthcare professionals in early detection and preventive measures for patients at risk of heart failure.
Future improvements can include:
- Exploring additional features and data sources.
- Implementing other machine learning algorithms.
- Conducting hyperparameter tuning for the Decision Tree model.
- Integrating the model into a web or mobile application for real-time predictions.
I hope this project provides valuable insights and serves as a foundation for further development in the field of medical data science.