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Heart diseases are considered the foremost cause of death within developing nations; therefore, prediction of heart disease is crucial for evaluating the risk of patients. This paper will introduce a new method to enhance prediction accuracy by combining CNNs with SAE for feature enhancement. Our method uses a dataset of 918 patient records with 11 clinical variables, and it removes the drawback of traditional classifiers by using feature augmentation to build more informative features. Experimental results show that our model’s accuracy is 93.478%, an improvement over traditional classifiers like MLP and RF by 4.98%. Latent space size is also optimized, and 100 best features are obtained. The results suggest that the deep learning methods, especially the combination of SAE with CNN, bring in notable benefits for heart disease prediction, which might further be used for the clinical purpose of earlier interventions.

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Feature Augmentation and Convolutional Neural Networks for Accurate Prediction of Heart Disease

  • 会议论文

  • 发表日期：

    2026年

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  • T. G. Ramnadh Babu,
  • Kolipaka Bhavana,
  • Gude Chaitanya,
  • Madagunda Sravani,
  • Rafi Shaik,
  • Sireesha Moturi

摘要

Heart diseases are considered the foremost cause of death within developing nations; therefore, prediction of heart disease is crucial for evaluating the risk of patients. This paper will introduce a new method to enhance prediction accuracy by combining CNNs with SAE for feature enhancement. Our method uses a dataset of 918 patient records with 11 clinical variables, and it removes the drawback of traditional classifiers by using feature augmentation to build more informative features. Experimental results show that our model’s accuracy is 93.478%, an improvement over traditional classifiers like MLP and RF by 4.98%. Latent space size is also optimized, and 100 best features are obtained. The results suggest that the deep learning methods, especially the combination of SAE with CNN, bring in notable benefits for heart disease prediction, which might further be used for the clinical purpose of earlier interventions.