Enhanced heart disease prediction through hybrid CNN-TLBO-GA optimization: a comparative study with conventional CNN and optimized CNN using FPO algorithm-Reference-Cited by-同舟云学术

Enhanced heart disease prediction through hybrid CNN-TLBO-GA optimization: a comparative study with conventional CNN and optimized CNN using FPO algorithm

Published:2024-08-03 Issue:1 Volume:11 Page:
ISSN:2331-1916
Container-title:Cogent Engineering
language:en
Short-container-title:Cogent Engineering

Author:

Ram Kumar R. P.¹^ORCID,Raju S.²,Annapoorna Errabelli³,Hajari Manu⁴,Hareesa Kadali⁴,Vatin Nikolai Ivanovich⁵^ORCID,Joshi Abhishek⁶,AL-Attabi Kassem⁷

Affiliation:

1. Department of Computer Science and Engineering (AI & ML), Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, India

2. Department of Information Technology, Mahendra Engineering College (Autonomous), Namakkal, Tamilnadu, India

3. Department of CSBS, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, India

4. Department of Computer Science and Engineering (Data Science), Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, India

5. Division of Research and Development, Lovely Professional University, Phagwara, Punjab, India

6. Department of Engineering, Uttaranchal University, Dehradun, India

7. Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/23311916.2024.2384657

Reference25 articles.

1. Prediction of Heart Disease Based on Machine Learning Using Jellyfish Optimization Algorithm

2. Design and development of mixed integer programming model for scheduling tasks through artificial intelligence

3. Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital

4. Enhancing Heart Disease Prediction through Ensemble Learning Techniques with Hyperparameter Optimization

5. Machine Learning-Based Model to Predict Heart Disease in Early Stage Employing Different Feature Selection Techniques