Heart Failure Prediction Using Machine Learning

Introduction

Heart failure is a chronic condition where the heart is unable to pump blood efficiently, leading to severe health complications. Early detection is crucial for effective treatment and management. This project leverages machine learning techniques to predict heart failure from patient data.

Materials and Methods

• Dataset: The dataset used consists of several health indicators collected from patients, including age, gender, blood pressure, cholesterol levels, and other relevant health metrics.

• Preprocessing: Data preprocessing steps included handling missing values, normalizing numerical features, and encoding categorical variables. These steps ensured that the data was clean and suitable for training machine learning models.

• Exploratory Data Analysis (EDA): EDA techniques were employed to understand the distribution and relationships within the data. Visualization tools like histograms, scatter plots, and correlation matrices were used to gain insights.

• Machine Learning Models: Several machine learning algorithms were evaluated, including Logistic Regression, Decision Trees, Random Forest, and Gradient Boosting. Each model’s performance was assessed using metrics such as accuracy, precision, recall, and F1-score.

Results and Discussion

The Gradient Boosting model achieved the highest performance, with an accuracy of 85.64% and a recall score of 86.7%. This model was able to accurately predict heart failure cases, demonstrating its potential for early diagnosis and treatment.

Technologies Used

• Machine Learning Frameworks: Scikit-Learn, XGBoost
• Data Processing: Pandas, NumPy
• Visualization: Matplotlib, Seaborn

Conclusion

This project showcases the potential of machine learning in medical diagnostics. By accurately predicting heart failure, healthcare providers can take proactive measures to improve patient outcomes.

Publication

This work was published in the 2021 International Conference on Intelligent Technologies (CONIT).