Key result
CNN deep learning predicts heart failure outcomes with ~99% accuracy, outperforming standard machine learning.
Why the study?
Heart disease leads to heart failure, necessitating a tool for predicting survival.
Does a Convolutional Neural Network deep learning model improve prediction accuracy of heart failure outcomes compared to traditional machine learning models?
Does a Convolutional Neural Network deep learning model improve prediction accuracy of heart failure outcomes compared to traditional machine learning models?
Deep learning models like CNN can achieve high accuracy (99%) in predicting heart failure survival, outperforming traditional machine learning models.
Answers are grounded in this paper and cite their sources.
Does not support clinical adoption yet; leaves open deep learning's role in heart failure prediction pending prospective validation.
The heart is essential to human life, so it is important to protect it and understand any kind of damage it can have. All the diseases related to hearts leads to heart failure. To help address this, a tool for predicting survival is needed. This study explores the use of several classification models for forecasting heart failure outcomes using the Heart Failure Clinical Records dataset. The outcome contrasts a deep learning (DL) model known as the Convolutional Neural Network (CNN) with many machine learning models, including Random Forest (RF), K-Nearest Neighbors (KNN), Decision Tree (DT), and Naïve Bayes (NB). Various data processing techniques, like standard scaling and Synthetic Minority Oversampling Technique (SMOTE), are used to improve prediction accuracy. The CNN model performs best by achieving 99%. In comparison, the best-performing ML model, Naïve Bayes, reaches 92.57%. This shows that deep learning provides better predictions of heart failure, making it a useful tool for early detection and better patient care.
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Qadeer et al. (2025) studied Heart failure. Convolutional Neural Network (CNN) vs. Machine learning models (e.g., Naïve Bayes) was evaluated on Prediction accuracy for forecasting heart failure outcomes. A Convolutional Neural Network deep learning model achieved 99% accuracy in predicting heart failure outcomes, outperforming the best machine learning model (Naïve Bayes) at 92.57%.
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