Aim:

             The aim of this research is to enhance the detection of cardiovascular diseases in ECG images using advanced machine learning techniques.

Abstract:

            The accurate and timely detection of cardiovascular diseases through electrocardiogram (ECG) analysis is pivotal in modern healthcare. This study compares the effectiveness of existing and proposed methodologies for this purpose. The existing method utilizes a convolutional neural network (CNN) for feature extraction, followed by Naive Bayes for final prediction. In contrast, the proposed approach implements the VGG16 model for feature extraction. The novelty lies in employing an ensemble model comprising XG Boost, Random Forest, and Support Vector Classification (SVC) for predictive analysis. The transition from CNN to VGG16 in feature extraction aims to capture more intricate patterns and nuanced features present in ECG images. Furthermore, the ensemble of robust machine learning models such as XG Boost, Random Forest, and SVC facilitates a more comprehensive analysis and decision-making process, enhancing predictive accuracy and robustness. The experimental results, obtained through rigorous evaluation on a diverse dataset, demonstrate that the proposed methodology exhibits superior performance in terms of sensitivity, specificity, and overall accuracy compared to the existing approach. The ensemble of models presents a promising direction for enhancing the reliability and precision of cardiovascular disease detection in ECG images, offering a significant contribution to the field of medical diagnostics. This comparative analysis underscores the potential of advanced machine learning techniques in improving the early detection and diagnosis of cardiovascular diseases, offering a foundation for more effective clinical decision support systems in healthcare.

Proposed Method:

           The proposed method aims to address the limitations of the existing approach by employing the VGG16 model for more intricate feature extraction from ECG images. Moreover, an ensemble model composed of XG Boost, Random Forest, and Support Vector Classification (SVC) is utilized for prediction, enhancing the accuracy and robustness of disease detection.

Advantages:

• The transition from CNN to VGG16 allows for the extraction of more detailed and nuanced features from ECG images, potentially capturing complex patterns more effectively.
  

• The utilization of an ensemble model (XG Boost, Random Forest, SVC) combines the strengths of multiple algorithms, leading to improved predictive accuracy and robustness in identifying cardiovascular diseases.
  
• The proposed methodology is anticipated to exhibit increased sensitivity, specificity, and overall accuracy compared to the existing method, providing a more reliable diagnostic tool for healthcare professionals.