International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 18, No. 5, 8 Oct. 2026
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An Approach to Employ Content-based Recommendation Techniques in the Context of Cardiovascular Disease Detection and Prevention
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Author(s)
Index Terms
Machine Learning, Cardiovascular Disease, Recommendation, Stacking Ensemble, Cosine Similarity, TF-IDF
Abstract
Cardiovascular disease after post-COVID has become a life-threatening, deadly disease. A major percentage of the mortality rate occurring every year is due to heart-related diseases. India, being a middle-income nation, is facing a severe need for awareness and resources to reduce the untimely death, especially in the middle-aged population, due to cardiac arrest. Machine learning has been acting as an essential tool to predict an early occurrence of this fatal disease. There is a need to provide personalized recommendations to the patient if the patient is predicted to have heart disease. The paper aims at providing the various content-based recommendations to the patients based on 5 parameters: age, FBS, trestbps, chol, thalach, and CP so that the precautionary measures need to be taken by the person based on the recommendation provided by the model. The contribution of this work is summarized into three parts. a) A stacking ensemble-based meta-learner is developed to predict a person with heart disease. b) A machine learning pipeline is incorporated to automate the workflow of disease detection and c) A novel personalized recommendation method with respect to cardiovascular risk reduction and preventative measures providing the optimal solutions to the person suffering from heart disease and its prognosis. The proposed method is validated by providing the necessary recommendations to the patients, demonstrating significant risk reduction for individuals with high CVD risk. The result evaluation is done using a t-test showing a statistically significant level and an ROC curve with a value of 0.98 and a sensitivity analysis with a value of 0.91. The mean average precision (MAP) value is 0.75. Creating a machine learning-based model that forecasts the likelihood of heart disease onset is the aim of this research. The existence or absence of heart illness, given as a binary classification (0 = no heart disease, 1 = heart disease), is the outcome variable for this prediction task.
Cite This Paper
Arundhati Uplopwar, Rashmi Vashisth, Arvinda Kushwaha, "An Approach to Employ Content-based Recommendation Techniques in the Context of Cardiovascular Disease Detection and Prevention", International Journal of Information Technology and Computer Science(IJITCS), Vol.18, No.5, pp.1-15, 2026. DOI:10.5815/ijitcs.2026.05.01
Reference
[1]Victor Chang, Vallabhanent Rupa Bhavani, Ariel Qianwen Xu, MA Hossain, “An artificial intelligence model for heart disease detection using machine learning algorithms”, Healthcare Analytics 2(2022), 100016,https: //doi.org/ 10.1016/ j.health. 2022.100016.
[2]Henry C MCGill Jr,Alex Mc Mahan, Samuel S Gidding, “Preventing Heart Disease in 21st Century”, Vol.117, No9, 2021https://doi.org/10.1161/CIRCULATIONAHA.107.717033.
[3]A. Sreeniwas Kumar, Nakul Sinha, “Cardiovascular disease in India: A 360 degree overview”, MJAFI, Volume 76, Issue 1, pages1-3, 2020.
[4]Se Yong Jang,Jin Joo Park, Eric Adler, Emily Eshraghian, Faraz S Ahmed, Claudio Campagnari, Avi Yagil, Barry Greenberg, “Mortality Prediction in Patients With or Without Heart Failure Using a Machine Learning Model”, JACC: ADVANCES, VOL. 2, NO. 7, 10055, 2023.
[5]Aamir Javaid, Fawzi Zghyer, Chang Kim, Erin M Spaulding, Nino Isakadze, Jie Ding, Daniel Kargillis, Yumin Goo, Faisal Rehman, Donald E Brown, Suchi Saria, Seth S Martin, Christopher M Karmer, Roger S Blumenthal, Francoise A Marvel,” Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology”, AJPC, 12(2022)100379.
[6]Nadiah A Baghdadi, Sally Mohammed Farghaly Abdelaliem, Amer Malki, Ibrahim Gad, Ashraf Ewis & Elsayed atlam, “Advanced machine learning techniques for cardiovascular disease early detection and diagnosis”, Journal of Big Data,2023 10:144 https://doi.org/10.1186/s40537-023-00817-1,
[7] Xiaoyu Sun, Yuzhe Yin, Qiwei Yang & Tianqi Huol, “Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives”, European Journal of Medical Research, 2023, 28:242,https://doi.org/10.1186/s40001-023-01065-y.
[8]Pedro A. Moreno-S´anchez, Guadalupe Garcia-Isla, Valentina D A Corino, Antti Vehkaoja, Kirsten Brukamp, Mark Van Gils, Luca Mainardi, “ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review”, Computers in Biology and Medicine 172108235, 2024.
[9]Pradeep Kumar singh, Pijush Kanti Dutta Pramanik, Avick Kumar Dey and Prasenjit Choudhury, “Recommender systems: an overview, research trends, and future directions”, Int. J. Business and Systems Research, Vol. 15, No. 1, 2021.
[10]F.O. Isinkaye, Y.O Folajimi, B.A.Ojokoh, “Recommendation systems: Principles, methods and evaluation”, Egyptian Informatics Journal, Volume 16, Issue3, PP 261-273, 2015.
[11] Mahdi Nasiri, Behrouz Minaei, Amir Kaiani, “Dynamic Recommendation: Disease Prediction and Prevention Using Recommender System”, Int J Basic Sci Med, 1(1):13-17, 2016.
[12]Baha Ihnaini, M A Khan, Tahir Abbas Khan, Sagheer Abbas, Mohammad Sh Daoud, Munir Ahmed, Muhammad Adnan Khan,” A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep Ensemble Learning”, Computational Intelligence and Neuroscience Volume 2021, ArticleID4243700,11pages.https://doi.org/10.1155/2021/4243700.
[13]Robin De Croon, Leen Van Houdt, Nyi Nyi Htun, Gregor Stiglic, Vero Vanden Abeele, Katrien Verbert, “Health Recommender Systems: Systematic Review”, J Med Internet Res 2021, vol. 23, issue. 6, e18035, p.1.
[14]Wenbin Yue, Zidong Wang, Jieyu Zhang and Xiaohui Liu, “An Overview of Recommendation Techniques and their Applications in Healthcare”, Volume: 8, Issue 4, 2021.
[15]Chunxu Wang, Haiyan Wang, Jingwen Pi and Li An, “Park Recommendation Algorithm based on User Reviews and Ratings”, IJPE, vol. 15, no.3, March 2019, pp. 803-812 DOI: 10.23940/ijpe.19.03.p9.803812.
[16]Angel Arul Jothi J and Razia Sulthana A, “A Review on the Literature of Fashion Recommender System using Deep Learning”, IJPE, vol. 17, no. 8, pp. 695-702,2021, DOI: 10. 23940 /ijpe .21 .08. p5.695702.
[17]Anuja Arora and Anu Taneja, “Research Issues, Innovation and Associated Approaches for Recommendation on Social Networks”, IJPE, vol. 17, no. 12, pp. 1027-1036, 2021, DOI: 10.23940/ijpe.21.12.p7.10271036.
[18] Navin K and Mukesh Krishnan, “Holistic Recommendation System Framework for Health Care Programs”, in Sustainable Advanced Computing: Select Proceedings of ICSAC 2021, Springer, pp. 59-69, 2022. doi: 10.1007/978-981-16-9012-9_6.
[19]Ayse Dogan, Yuxuan Li, Chiwetalu Peter Odo, kalyani Sonawane, Ying Lin, Chenang Liu, “A utility-based machine learning-driven personalized lifestyle recommendation for cardiovascular disease prevention”, Journal of Biomedical Informatics, 141(2023)104342.
[20]Subhashini Narayan and E.Sathiyamoorthy, “A novel recommender system based on FFT with machine learning for predicting and identifying heart diseases”, Neural Computing and Applications (2019) 31 (Suppl 1):S93–S102 https://doi.org/10.1007/s00521-018-3662-3.
[21]Michael J.Pazzani and Daniel Billsus, “Content-Based Recommendation Systems, The Adaptive Web”, LNISA, volume 4321, pp 325-341, 2008.
[22]Sharon L. Mulvagh, Tracey J.F. Colella, Martha Gulati, Rebecca Crosier, Saleema Allana, Varinder Kaur Randhawa, Jill Bruneau, Christine Pacheco, Shahin Jaffer, Lisa Cotie, Emma Mensour, Marie-Annick Clavel, Braeden Hill, Amy A. Kirkham, Heather Foulds, Kiera Liblik, Andrea Van Damme, Sherry L. Grace, Karen Bouchard, Heather Tulloch, Helen Robert, April Pike, Jamie L. Benham, Nicole Tegg, Nazli Parast, Najah Adreak, Laurie-Anne Boivin-Proulx, Monica Parry, Zoya Gomes, Hope Sarfi, Chinelo Iwegim, Harriette G.C. Van Spall, Kara A. Nerenberg, Stephen P. Wright, Jayneelkumar A. Limbachia, Kerri-Anne Mullen, and Colleen M. Norris, “The Canadian Women’s Heart Health Alliance ATLAS on the Epidemiology, Diagnosis, and Management of Cardiovascular Disease in Women — Chapter 9: Summary of Current Status, Challenges, Opportunities, and Recommendations,” CJC Open, vol. 6, no. 2, pp. 258-278, 2024. doi: 10.1016/j.cjco.2023.12.001.
[23]Thi Ngoc Trang Tran, Alexander Felfernig, Christoph Trattner and Andreas Holzinger,” Recommender systems in the healthcare domain: state-of-the-art and research issues”, Journal of Intelligent Information Systems 57:171–201, 2021.
[24]Zeshan Fayyaz, Mahsa Ebrahimian, Dina Nawara, Ahmed Ibrahim and Rasha Kashef, “Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities”, Appl.Sci. 2020, 10(21), 748.https://doi.org/10.3390/app10217748.
[25]F. Amato, V. Moscato, A. Picariello, and F. Piccialli, “Sos: a multimedia recommender system for online social networks,” Future generation computer systems, vol. 93, pp. 914–923, 2019.
[26]Asmae Bentaleb, Younes EI Bouzekri, Ayoub Ait Lahcen, “A Review on Content Based Recommender Systems in Tourism, ICDTA 2021: Digital Technologies and Applications”, pp 527–541, 2021.
[27]P. Chinnasamy, Wing-Keung Wong, A Ambeth Raja, Osamah Ibrahim Khalaf, Ajmeera Kiran,J Chinna Babu, “Health Recommendation System using Deep Learning-based collaborative Filtering”, Heliyon, volume 9, Issue 12, 2023.
[28]Samira Khademzadeh, Marjan Ghazisaeidi, Mohsen Nassiri Toosi, Arash Roshanpoor, Esmaeil Mehraeen, “A Intelligent Recommender System for people who are prone to fatty liver disease”, Informatics in Medical Unlocked, volume 41, 2023,101315.
[29]Anam Mustaqeem, Syed Muhammad Anwar, Abdul Rashid Khan, Muhammad Majid,” A statistical analysis based recommender model for heart disease patients”, IJMI, volume 108, 2017, pp 134-145.
[30]Mahalingam P.R and Dheeba J, “A heart disease prognosis pipeline for edge using federated learning”, AIEEEE,7(2024),100490