This project leverages the Decision Tree algorithm to predict heart failure outcomes based on patient data. The approach highlights the effectiveness of Decision Trees in identifying critical patterns in medical datasets. This repository includes the dataset, code, and detailed documentation to replicate and understand the prediction process.
This repository contains a Jupyter Notebook (heart_failure_prediction.ipynb) for predicting heart failure outcomes using the Decision Tree algorithm. The dataset used in this project was acquired from Kaggle.
Methodology Data Preprocessing: Cleaned and normalized the dataset, handling missing values for improved data quality.
Model Training: Applied the Decision Tree algorithm to predict heart failure based on features like age, ejection fraction, serum creatinine, etc.
Model Evaluation: Split the data into training (70%) and testing (30%) sets to evaluate model performance. Achieved an accuracy of 99% in predicting heart failure.
Insights Binary Classification: Decision Tree demonstrated strong performance as a binary classifier, effectively distinguishing between patients with and without heart failure.
Future Work: Explore ensemble methods like Random Forests and Gradient Boosting to enhance model robustness. Incorporate additional features such as lifestyle factors and genetic information for more personalized predictions. Usage Clone the Repository:
bash Copy code git clone https://github.com/yourusername/heart-failure-prediction.git Launch Jupyter Notebook:
bash Copy code cd heart-failure-prediction jupyter notebook Open and Run the Notebook: Open heart_failure_prediction.ipynb in Jupyter Notebook, and execute each cell to replicate the analysis.
This project demonstrates a straightforward approach to predicting heart failure using the Decision Tree algorithm, highlighting its potential in medical data analysis and patient risk assessment.