Clinical Healthcare Applications: Efficient Techniques for Heart Failure Prediction Using Novel Ensemble Model

Heart failure is a severe medical ailment that significantly impacts patients’ well-being and the healthcare system. For improved results, early detection and immediate treatment are essential. This work aims to develop and evaluate predictive models by applying sophisticated ensemble learning techniques. In order to forecast heart failure, we used a clinical dataset from Kaggle. We used the well-known ensemble techniques of bagging and random forest (RF) to create our models. With a predicted accuracy of 82.74%, the RF technique, renowned for its versatility and capacity to handle complex data linkages, fared well. The bagging technique, which employs several models and bootstrapped samples, also demonstrated a noteworthy accuracy of 83.98%. The proposed model achieved an accuracy of 90.54%. These results emphasize the value of group learning in predicting cardiac failure. The area under the ROC curve (AUC) was another metric to assess the model’s discriminative ability, and our model achieved 94% AUC. This study dramatically improves the prognostic modeling for heart failure. The findings have extensive implications for clinical practice and healthcare systems and offer a valuable tool for early detection and intervention in cases of heart failure.