Scholarlink

<p>CNN-LSTM and CNN + ANN hybrid deep learning models are compared to standard machine learning classifiers for heart disease prediction. By combining CNN feature extraction with ANN pattern recognition, the CNN + ANN hybrid model is created. This mode makes CNNs appropriate for ECG and heart scan feature extraction since they process and analyse visual input well. This model layer can identify patterns and structures in photos that are associated with heart disease. Recognition of ANN patterns. These traits let ANN recognize complicated patterns and correlations. CNN’s complex features suit ANNs’ non-linear data knowledge. CNN/ANN detect and measure heart disease risk factors. Output usually indicates cardiac disease severity or risk.</p><p>We demonstrate hybrid models’ superiority using a well-selected dataset and several input features. These models outperform SVM, Naïve Bayes, and Decision Trees in accuracy, precision, recall, and F-measure. With its outstanding spatial-temporal data interpretation, the CNN-LSTM model expands clinical diagnostic applications. Our findings propose using hybrid designs across medical disorders to advance predictive diagnostics. Develop these models for real-time diagnostic applications to speed up and enhance medical processes. We emphasize de-mystifying these algorithms’ complex inner workings to promote transparency and trust in their treatment suggestions. According to studies, integrating these advanced models with IoT technology might enhance continuous health monitoring, providing real-time data and predictive analytics to better detect and treat heart illness. Our research improves patient care and clinical procedures by developing more robust, efficient, and comprehensive disease prediction and management systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Evaluating Hybrid Architectures: Comparing CNN-LSTM Models Against Traditional Approaches in Heart Disease Prediction

  • 期刊论文

  • 发表日期：

    2025年8月23日

查看全文
  • Lam Rathnakumari,
  • Ganga RamaKoteswara Rao

摘要

CNN-LSTM and CNN + ANN hybrid deep learning models are compared to standard machine learning classifiers for heart disease prediction. By combining CNN feature extraction with ANN pattern recognition, the CNN + ANN hybrid model is created. This mode makes CNNs appropriate for ECG and heart scan feature extraction since they process and analyse visual input well. This model layer can identify patterns and structures in photos that are associated with heart disease. Recognition of ANN patterns. These traits let ANN recognize complicated patterns and correlations. CNN’s complex features suit ANNs’ non-linear data knowledge. CNN/ANN detect and measure heart disease risk factors. Output usually indicates cardiac disease severity or risk.

We demonstrate hybrid models’ superiority using a well-selected dataset and several input features. These models outperform SVM, Naïve Bayes, and Decision Trees in accuracy, precision, recall, and F-measure. With its outstanding spatial-temporal data interpretation, the CNN-LSTM model expands clinical diagnostic applications. Our findings propose using hybrid designs across medical disorders to advance predictive diagnostics. Develop these models for real-time diagnostic applications to speed up and enhance medical processes. We emphasize de-mystifying these algorithms’ complex inner workings to promote transparency and trust in their treatment suggestions. According to studies, integrating these advanced models with IoT technology might enhance continuous health monitoring, providing real-time data and predictive analytics to better detect and treat heart illness. Our research improves patient care and clinical procedures by developing more robust, efficient, and comprehensive disease prediction and management systems.