Heart Failure Prediction using Machine Learning

The main objective of this project is to develop a machine learning-based system capable of predicting the likelihood of heart failure in patients using clinical data. By analyzing parameters such as age, ejection fraction, serum creatinine, blood pressure, and other physiological indicators, the system aims to assist healthcare professionals in early identification of patients at risk, enabling timely medical intervention.

The project is developed using Python as the main programming language for model training and prediction. The Flask framework is used for backend development to connect the machine learning models with the front end. The front-end interface is created using HTML, CSS, and JavaScript for dataset upload, visualization, and result display.

The dataset used in this project is a clinical dataset containing 5,000 patient records. Each record includes medical and demographic attributes such as age, anaemia, creatinine phosphokinase, diabetes, ejection fraction, high blood pressure, platelets, serum creatinine, serum sodium, sex, smoking, time, and DEATH_EVENT. The dataset helps the system learn patterns associated with heart failure outcomes.

The project uses two powerful machine learning models: Stacking Classifier and XGBoost (Extreme Gradient Boosting) Classifier. The Stacking Classifier combines the predictions of multiple base learners to improve classification accuracy through ensemble learning. The XGBoost Classifier is a gradient boosting algorithm optimized for speed and performance, which constructs an ensemble of decision trees to minimize classification errors.

Both the Stacking Classifier and the XGBoost Classifier achieved a training accuracy of 99% and a testing accuracy of 99%, indicating excellent predictive performance and strong generalization capability. These results confirm the reliability of the system for heart failure prediction.

The system follows a systematic workflow. First, the user uploads a dataset in .csv format through the web interface. The Flask backend then validates the file and performs preprocessing tasks such as handling missing values, encoding categorical features, and normalizing data. The cleaned data is passed into the trained machine learning models, which predict the likelihood of heart failure for each patient record. The prediction results and performance metrics are then displayed on the front end, accompanied by visual graphs for better interpretation.

The system supports dataset upload, real-time prediction, performance visualization, and model comparison. It includes automated data preprocessing, integrates high-accuracy machine learning models, and provides interactive charts for analytical understanding. It is also web-deployed, allowing users to interact with it through any modern browser without requiring specialized software.

Flask acts as the middleware connecting the backend machine learning models with the front-end user interface. It handles routing, request processing, and communication between the uploaded dataset and the trained models. Flask ensures that user inputs (such as uploaded data) are processed correctly and that model predictions are sent back to the web interface for visualization.

XGBoost was selected due to its high efficiency, scalability, and strong predictive performance. It uses gradient boosting with regularization, which helps reduce overfitting and improves generalization. XGBoost is particularly effective in handling structured datasets like clinical data and is widely used in healthcare prediction tasks for its speed and accuracy.

The Stacking Classifier combines multiple base models to improve the overall predictive capability. Instead of relying on a single algorithm, it uses a meta-model to integrate the strengths of various classifiers. This layered ensemble approach leads to higher stability and accuracy, making it ideal for complex medical datasets where relationships among variables can be non-linear and multifactorial.

Data visualizations are used to make the analytical results more understandable and informative. The system displays multiple charts such as age distribution, gender ratio, comorbidity analysis, model accuracy comparison, and death event distribution. These charts help users visually interpret trends, correlations, and prediction outcomes, aiding in better medical insights and data-driven decision-making.

Before training or prediction, the system performs several preprocessing steps including handling missing values, encoding categorical variables, scaling numerical features, and splitting data into training and testing sets. This process ensures that the dataset is clean, consistent, and suitable for accurate machine learning predictions. Automated preprocessing also reduces the chances of human error and enhances model performance.

The models are evaluated using various metrics such as Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. These metrics collectively provide a comprehensive understanding of the system’s predictive power and its ability to balance false positives and false negatives, which is crucial for healthcare predictions.

Yes, the system supports real-time prediction. Once a data is uploaded through the Flask web interface, predictions are processed immediately, and results are displayed on the web page within seconds. This feature enables healthcare professionals to analyze patient data quickly and obtain risk predictions in real time.

The existing traditional diagnostic methods rely heavily on manual analysis and physician experience, which can be time-consuming and subjective. In contrast, this system leverages machine learning algorithms to analyze large datasets, identify hidden patterns, and make objective predictions based on data. It also provides visual insights and web-based accessibility, making it more efficient, accurate, and user-friendly.

The project contributes to healthcare by providing a data-driven decision-support tool that helps in early identification of heart failure risks. By automating predictions and simplifying data interpretation, it assists medical practitioners in making faster and more informed decisions, ultimately improving patient care and reducing the chances of late diagnosis.