Detection of Cardiovascular Diseases in ECG Images Using Machine Learning and Deep Learning Methods

Detection of Cardiovascular Diseases in ECG Images Using Machine Learning and Deep Learning Methods

₹5,500.00
Product Code: Python - Deep Learning
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Product Description

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.


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Package Includes

Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support


The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

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  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.