Heart failure is a global health issue that contributes significantly to morbidity and mortality. The illness develops when the heart cannot adequately pump blood, leaving the body with inadequate blood flow to meet its requirements. Early detection and prompt management are essential for enhancing patient outcomes and lessening the strain on healthcare systems.
The primary goal of this project is to develop a machine learning model capable of predicting the survival of patients diagnosed with heart failure. By leveraging clinical data, we aim to build a predictive model that can identify high-risk patients and assist healthcare professionals in making informed decisions.
We utilized a dataset containing various clinical features of heart failure patients. The dataset includes attributes such as age, sex, blood pressure, serum creatinine levels, ejection fraction, and more. Data cleaning processes were implemented to handle missing values, outliers, and ensure data normalization, making the dataset suitable for machine learning algorithms.
Feature engineering involved creating new features from the existing ones to enhance the predictive power of the model. We generated interaction features to capture the combined effects of different clinical attributes and polynomial features to account for non-linear relationships in the data. Additionally, L1 (Lasso) and L2 (Ridge) regularization techniques were applied to prevent overfitting and improve model performance.
Multiple machine learning models were built, including Logistic Regression, Decision Trees, Random Forest, and a Stacking model that combines the strengths of individual models. The models were trained and validated using a robust cross-validation technique to ensure generalizability and accuracy.
The performance of each model was evaluated using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Hyperparameter tuning was performed using grid search to optimize the models' performance. The Random Forest model emerged as the most effective predictor of patient survival, demonstrating the highest accuracy and ROC AUC scores among all the models evaluated.
The Random Forest model achieved an accuracy of 0.92, the highest among all the models evaluated. It also had a precision of 0.89, recall of 0.90, F1 score of 0.89, and an ROC AUC score of 0.92. These metrics indicate that the model correctly predicts the survival status of patients with high reliability.
The Random Forest model's feature importance analysis identified key predictors contributing to patient survival, providing insights into the clinical significance of each feature. The key features include serum creatinine levels, ejection fraction, and age.
Grid search was used for hyperparameter tuning to find the best combination of parameters for each model. This process involved evaluating all possible combinations of the provided hyperparameter values, training the model, and selecting the combination that yielded the best performance.
The Random Forest model holds significant potential in assisting healthcare professionals by identifying high-risk heart failure patients. By predicting patient survival, the model can help improve patient outcomes through timely and targeted interventions.
Predictive models enable the development of personalized treatment plans and efficient allocation of healthcare resources. Early prediction allows healthcare professionals to implement timely interventions, improving patient management and reducing unnecessary hospitalizations.
Future research can explore more advanced feature engineering techniques and incorporate additional data sources such as genetic information, lifestyle factors, and more detailed medical history to improve the model's accuracy. Developing models with greater interpretability, such as explainable AI techniques, can help clinicians better understand the decision-making process of the model.