Sheng-Shou, H. U. et al. Report on cardiovascular health and diseases in china 2021: an updated summary. Journal of Geriatric Cardiology 20(6), 399–430 (2023).
Google Scholar
Ciumărnean, L. et al. Cardiovascular risk factors and physical activity for the prevention of cardiovascular diseases in the elderly. International Journal of Environmental Research and Public Health 19(1), 207 (2021).
Google Scholar
Shariatnia, S. et al. Modeling the diagnosis of coronary artery disease by discriminant analysis and logistic regression: a cross-sectional study. BMC medical informatics and decision making 22(1), 85 (2022).
Google Scholar
Ayon, S. I., Islam, M. M. & Hossain, M. R. Coronary artery heart disease prediction: a comparative study of computational intelligence techniques. IETE Journal of Research 68(4), 2488–2507 (2022).
Google Scholar
Muhammad, L. J. & Algehyne, E. A. Fuzzy based expert system for diagnosis of coronary artery disease in nigeria. Health and technology 11(2), 319–329 (2021).
Google Scholar
Amarbayasgalan, T., Pham, V., Theera-Umpon, N., Piao, Y. & Ryu, K. H. An efficient prediction method for coronary heart disease risk based on two deep neural networks trained on well-ordered training datasets. IEEE Access 9, 135210–135223 (2021).
Nahar, J., Imam, T., Tickle, K. S. & Chen, Y. P. Association rule mining to detect factors which contribute to heart disease in males and females. Expert systems with applications 40(4), 1086–1093 (2013).
Srinivasan, S. et al. An active learning machine technique based prediction of cardiovascular heart disease from UCI-repository database. Scientific Reports 13(1), 13588 (2023).
Google Scholar
Juenger, J. et al. Health related quality of life in patients with congestive heart failure: comparison with other chronic diseases and relation to functional variables. Heart 87(3), 235–241 (2002).
Google Scholar
Le, J., Dorstyn, D. S., Mpfou, E., Prior, E. & Tully, P. J. Health-related quality of life in coronary heart disease: a systematic review and meta-analysis mapped against the international classification of functioning, disability and health. Quality of life research 27, 2491–2503 (2018).
Google Scholar
Mei, Y. et al. Health-related quality of life and its related factors in coronary heart disease patients: results from the henan rural cohort study. Scientific reports 11(1), 5011 (2021).
Google Scholar
Tušek-Bunc, K. & Petek, D. Comorbidities and characteristics of coronary heart disease patients: their impact on health-related quality of life. Health and quality of life outcomes 14, 1–12 (2016).
Google Scholar
Nusinovici, S. et al. Logistic regression was as good as machine learning for predicting major chronic diseases. Journal of clinical epidemiology 122, 56–69 (2020).
Google Scholar
Babiker, S. et al. Logit model in prospective coronary heart disease (CHD) risk factors prediction in saudi population. Saudi Journal of Biological Sciences 28(12), 7027–7036 (2021).
Google Scholar
Pan, H., Li, J. & Wei, Z. Mining interesting association rules in medical images. In Advanced Data Mining and Applications: First International Conference. Proceedings 1, pages 598–609. Springer, (2005).
Wang, C. et al. Exploration of the association rules mining technique for the signal detection of adverse drug events in spontaneous reporting systems. PloS one 7(7), e40561 (2012).
Google Scholar
Simon, G. Y., Schrom, J., Castro, M. R., Li, P. W. & Caraballo, P. J. Survival association rule mining towards type 2 diabetes risk assessment. In AMIA annual symposium proceedings, volume 2013, page 1293. American Medical Informatics Association, (2013).
Mahmood, S., Shahbaz, M. & Guergachi, A. Negative and positive association rules mining from text using frequent and infrequent itemsets. The Scientific World Journal 2014(1), 973750 (2014).
Google Scholar
Ping-Hsun, L. et al. An apriori algorithm-based association rule analysis to identify herb combinations for treating uremic pruritus using chinese herbal bath therapy. Evidence-Based Complementary and Alternative Medicine 2020(1), 8854772 (2020).
Cuerda, M. C. et al. Development and validation of a specific questionnaire to assess health-related quality of life in patients with home enteral nutrition: Nutriqol® development. Patient preference and adherence, pages 2289–2296, (2016).
Hastie, T., Tibshirani, R., Friedman, J. H. & Friedman, J. H. The elements of statistical learning: data mining, inference, and prediction, volume 2. Springer, (2009).
Kumbhare, T. A. & Chobe, S. V. An overview of association rule mining algorithms. International Journal of Computer Science and Information Technologies 5(1), 927–930 (2014).
Google Scholar
Altaf, W., Shahbaz, M. & Guergachi, A. Applications of association rule mining in health informatics: a survey. Artificial Intelligence Review 47, 313–340 (2017).
Google Scholar
Payus, C., Sulaiman, N., Shahani, M. & Bakar, A. A. Association rules of data mining application for respiratory illness by air pollution database. Int J Basic Appl Sci 13(3), 11–16 (2013).
Sharma, N. & Om, H. Significant patterns for oral cancer detection: association rule on clinical examination and history data. Network Modeling Analysis in Health Informatics and Bioinformatics 3, 1–13 (2014).
Google Scholar
Huang, Y., Yaqi, S., Byun, Y., Lee, Y. & Kim, S. Analysis of multiple chronic disease characteristics in middle-aged and elderly south koreans by exercise habits based on association rules mining algorithm. BMC Public Health 23(1), 1232 (2023).
Google Scholar
Burkauskas, J. et al. Exploring cognitive concomitants of mental fatigue in patients with coronary artery disease. Neuropsychobiology 76(3), 151–160 (2018).
Lao, X. Q. et al. Sleep quality, sleep duration, and the risk of coronary heart disease: a prospective cohort study with 60,586 adults. Journal of Clinical Sleep Medicine 14(1), 109–117 (2018).
Google Scholar
Sundbøll, J. et al. Constipation and risk of cardiovascular diseases: a danish population-based matched cohort study. BMJ open 10(9), e037080 (2020).
Google Scholar
Hashimoto, S. et al. A lower maximum bite force is a risk factor for developing cardiovascular disease: the suita study. Scientific Reports 11(1), 7671 (2021).
Google Scholar
Vermorken, A. J. M., Andrès, E. & Cui, Y. Bowel movement frequency, oxidative stress and disease prevention. Molecular and clinical oncology 5(4), 339–342 (2016).
Google Scholar
Deb, B., Prichard, D. O. & Bharucha, A. E. Constipation and fecal incontinence in the elderly. Current gastroenterology reports 22, 1–16 (2020).
Google Scholar
Pavy, B. et al. Therapeutic education in coronary heart disease: position paper from the working group of exercise rehabilitation and sport (gers) and the therapeutic education commission of the french society of cardiology. Archives of Cardiovascular Diseases 106(12), 680–689 (2013).
Google Scholar
Kim, Y. S., Song, B. K., Ji Sun, O. & Woo, S. S. Aerobic exercise improves gastrointestinal motility in psychiatric inpatients. World Journal of Gastroenterology: WJG 20(30), 10577 (2014).
Google Scholar
Lai, H. et al. Effects of dietary fibers or probiotics on functional constipation symptoms and roles of gut microbiota: a double-blinded randomized placebo trial. Gut Microbes 15(1), 2197837 (2023).
Google Scholar
Jaspan, V. N. et al. The role of sleep in cardiovascular disease. Current atherosclerosis reports, pages 1–14, (2024).
Mehra, R. Sleep apnea and the heart. Cleve Clin J Med 86(9 Suppl 1), 10–18 (2019).
Google Scholar
Deraz, O. et al. Oral condition and incident coronary heart disease: A clustering analysis. Journal of Dental Research 101(5), 526–533 (2022).
Google Scholar
Hopkins, S. et al. Oral health and cardiovascular disease. The American Journal of Medicine, (2023).
Shirobe, M. et al. Effect of an oral frailty measures program on community-dwelling elderly people: a cluster-randomized controlled trial. Gerontology 68(4), 377–386 (2022).
Google Scholar
Vaccarino, V. & Douglas Bremner, J. Behavioral, emotional and neurobiological determinants of coronary heart disease risk in women. Neuroscience & Biobehavioral Reviews 74, 297–309 (2017).
Google Scholar
Cífková. R. Gender differences in secondary prevention of coronary heart disease: Far from closing the gap, (2022).
AlBadri, A. et al. Sex differences in coronary heart disease risk factors: rename it ischaemic heart disease!, (2017).
Tillmann, T. et al. Education and coronary heart disease: mendelian randomisation study. bmj, 358, (2017).
Schiborn, C. et al. A newly developed and externally validated non-clinical score accurately predicts 10-year cardiovascular disease risk in the general adult population. Scientific Reports 11(1), 19609 (2021).
Google Scholar
Yan, J. et al. A clinical decision support system for predicting coronary artery stenosis in patients with suspected coronary heart disease. Computers in Biology and Medicine 151, 106300 (2022).
Google Scholar
Wang, L. et al. Profile and correlates of health-related quality of life in chinese patients with coronary heart disease. Chinese medical journal 128(14), 1853–1861 (2015).
Google Scholar
Yung, L. M. et al. Exercise, vascular wall and cardiovascular diseases: an update (part 2). Sports Medicine 39, 45–63 (2009).
Google Scholar
link