International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 18, No. 1, 8 Feb. 2026
Cover page and Table of Contents: PDF (size: 1293KB)
Personalized Cardiovascular Risk Reduction: A Hybrid Recommendation Approach Using Generative Adversarial Networks and Machine Learning
PDF (1293KB), PP.17-27
Views: 0 Downloads: 0
Author(s)
Index Terms
Machine Learning, Cardiovascular, Meta Learner, Recommendation, GAN
Abstract
Cardiovascular disease (CVD) is a leading cause of death worldwide and hence requires early risk assessment and focused preventative measures. The study describes a novel two-phase hybrid approach that combines machine learning-based CVD risk prediction and personalized lifestyle advice. In the first phase, cardiovascular risk is estimated using ensemble classifier that combines Random Forest Classifier, SVM and LR using metal learner trained on the Heart Disease dataset (1000 record, 14 attributes) has excellent predictive accuracy. In the second phase, optimization framework produces lifestyle suggestions that are safe for health within clinically determined parameters, which are enhanced using a hybrid recommendation system that combines content-based and Cluster-based Outcome Analysis. The suggested approach considerably outperformed a baseline of general lifestyle recommendations in a simulated high-risk cohort, exhibiting an average relative risk reduction of [X] % over a 10-year period as determined by the Framingham Risk Score. The suggested approach is made to be validated in future research using external datasets, simulated patient trials, and physician evaluation in order to guarantee clinical relevance This methodology highlights the promise for precision cardiovascular prevention by providing personalized, data-driven lifestyle recommendations.
Cite This Paper
Arundhati Uplopwar, Rashmi Vashisth, Arvinda Kushwaha, "Personalized Cardiovascular Risk Reduction: A Hybrid Recommendation Approach Using Generative Adversarial Networks and Machine Learning", International Journal of Intelligent Systems and Applications(IJISA), Vol.18, No.1, pp.17-27, 2026. DOI:10.5815/ijisa.2026.01.02
Reference
[1]Dorairaj Prabhakaran et al, Cardiovascular Diseases in India Compared with the United States, VOL. 72, NO. 1, 2018
[2]Francesco Santoro1 et al, Risk prediction of major cardiac adverse events and all-cause death following covid-19 hospitalization at one year follow-up: The HOPE-2 score, Volume 124,P108-114June 2024
[3]Bryan G. Pearson et al, Early, rapidly progressive vasculopathy in a transplanted heart: A possible complication of COVID-19, Volume 72, September–October 2024, 107661
[4]Ayse Dogan et al, A utility-based machine learning-driven personalized lifestyle recommendation for cardiovascular disease prevention, Volume 141, May 2023, 104342
[5]Pradeep Kumar singh et al(2021), Recommender systems: an overview, research trends, and future directions, Int. J. Business and Systems Research, Vol. 15, No. 1.
[6]Mayank Dalakoti et al, Incorporating AI into Cardiovascular disease prevention-insights from Singapore
[7]Chih-Lin Chi et al, Individualized patient-centered lifestyle recommendations: An expert system for communicating patient specific cardiovascular risk information and prioritizing lifestyle options, Journal of Biomedical Informatics, Volume 45, Issue 6, December 2012, pages 1164—1174.
[8]Chunxu Wang et al, Park Recommendation Algorithm based on User Reviews and Ratings, vol. 15, no. 3, March 2019, pp. 803-812.
[9]Jonathan Gemmell et al, Resource recommendation in social annotation systems: A linear-weighted hybrid approach, Volume 78, Issue 4, July 2012, pp 1160-1174.
[10]F.T.Greenway et al, Hybrid mHealth care: Patient perspectives of blended treatments for psychosis. A systematic review, Volume 274, 2024, pp1-10.
[11]Adrian Alonso Barriuso et al, Recommendation system of scientific articles from discharge summaries, volume 136, Part B, 2024.
[12]Jon Nicolas Bondevik et al, A systematic review on food recommender systems, Volume 238, part E, 2024,122166.
[13]Abdul Hannan et al, Machinelearning-basedsmartwearablesystemforcardiacarrestmonitoring using hybrid computing, BMSP, Volume 87, part-B, 2024,105519.
[14]Mojtaba Salehi and Isa Nakhai, A Hybrid Attribute-based Recommender System for E-learning Material Recommendation, IERI Procedia 2 (2012) 565 – 570.
[15]Yonghong et al, College Library Personalized Recommendation System Based on Hybrid Recommendation Algorithm, Procedia CIRP 83 (2019) 490–494.
[16]Steven S.Fu et al, Evaluating chronic disease approaches to ameliorate tobacco-related health disparities: Study protocol of a hybrid type 1 implementation-effectiveness trial, Volume 42, 2024,101380.
[17]Samira Khademzadeh et al, an intelligent recommender system for people who are prone to fatty liver disease, volume 41, 2023,101315.
[18]R. Sivasankari and J. Dhilipan, Hybrid scientific article recommendation system with COOT optimization, Data science and Management, 7, 2024, 99-107.
[19]Intifa Aman Taifa et al, A hybrid approach with customized machine learning classifiers and multiple feature extractors for enhancing diabetic retinopathy detection, Healthcare Analytics,5,2024,100346.
[20]Shilpa Balan and Sumali Conlon, A Study of Health Recommender Systems: Analyzing the Advancement of Sustainable Health Recommender Systems Using NLP Techniques, Journal of Strategic Innovation and Sustainability Vol. 17(3) 2022.