Advanced Heart Disease Prediction: Deep Learning-Enhanced Convolutional Neural Network in the Internet of Medical Things Environment

Main Article Content

Aarti Karandikar, Komal Jaisinghani, Piyush K.Ingole, Nilesh Shelke, Rupa A. Fadnavis, Navnath Narawade

Abstract

Using the large dataset from the UCI Machine Learning Repository, this study presents a state-of-the-art Hybrid Convolutional Neural Network (HCNN) for predicting heart disease. The HCNN design has convolutional layers, leftover blocks, and attention methods. These help pull more features from cardiovascular health data, even when the patterns are very complex. Utilizing these deep learning parts, the HCNN shows better prediction skills, getting higher accuracy, strong classification (AUC), and a fair F1 Score. Because the model can change to find complicated connections in the information, it could be used to make medical diagnosis better.  The HCNN is unique because it can instantly learn hierarchical structures from raw data. This lets it find hidden features that are important for accurately predicting heart disease. The convolutional layers help the model find local patterns, and the residue blocks stop problems with disappearing gradients, which makes training for deep designs more efficient. Attention processes make the network even more focused on important traits, which adds to its amazing ability to tell them apart. This work opens the door for researchers, doctors, and data scientists to use deep learning, and especially HCNN, to improve cardiovascular health analytics. This study takes a big step toward more accurate and efficient heart disease prognosis by giving a complete overview of the model's architecture and performance metrics. This opens the door for more research and use of advanced neural networks in the field of predictive medical diagnostics.

Article Details

Section
Articles
Author Biography

Aarti Karandikar, Komal Jaisinghani, Piyush K.Ingole, Nilesh Shelke, Rupa A. Fadnavis, Navnath Narawade

[1]Aarti Karandikar

2Komal Jaisinghani

3Dr. Piyush K. Ingole

4Nilesh Shelke

5Rupa A. Fadnavis 

6Dr. Navnath Narawade

 

[1]Department of Computer Science and Engineering (Data Science), Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India. Email: karandikara@rknec.edu

2Department of Computer Engineering, St. Vincent Pallotti College of Engineering and Technology, Nagpur, India. Email: 10584.komal@gmail.com

3Department of Computer Science and Engineering, Jhulelal Institute of Technology, Nagpur, Maharashtra, India. Email: piyush.ingole@gmail.com

4Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, Maharashtra, India. Email: nilesh.shelke@sitnagpur.siu.edu.in

5Department of Computer Science and Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India. Email: rafatycce@gmail.com

6Professor in Electronics and Telecommunication Engineering, Parvatibai Genba Moze College of Engineering Wagholi, Pune, Maharashtra, India. Email: nsnarawade@gmail.com

Copyright © JES 2024 on-line : journal.esrgroups.org

 

[1]Department of Computer Science and Engineering (Data Science), Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India. Email: karandikara@rknec.edu

2Department of Computer Engineering, St. Vincent Pallotti College of Engineering and Technology, Nagpur, India. Email: 10584.komal@gmail.com

3Department of Computer Science and Engineering, Jhulelal Institute of Technology, Nagpur, Maharashtra, India. Email: piyush.ingole@gmail.com

4Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, Maharashtra, India. Email: nilesh.shelke@sitnagpur.siu.edu.in

5Department of Computer Science and Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India. Email: rafatycce@gmail.com

6Professor in Electronics and Telecommunication Engineering, Parvatibai Genba Moze College of Engineering Wagholi, Pune, Maharashtra, India. Email: nsnarawade@gmail.com

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