{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T19:07:41Z","timestamp":1790190461371,"version":"4.1.0"},"reference-count":59,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T00:00:00Z","timestamp":1657152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nutrients"],"abstract":"<jats:p>The recent elevation of cases infected from novel COVID-19 has placed the human life in trepidation mode, especially for those suffering from comorbidities. Most of the studies in the last few months have undeniably raised concerns for hypertensive patients that face greater risk of fatality from COVID-19. Furthermore, one of the recent WHO reports has estimated a total of 1.13 billion people are at a risk of hypertension of which two-thirds live in low and middle income countries. The gradual escalation of the hypertension problem andthe sudden rise of COVID-19 cases have placed an increasingly higher number of human lives at risk in low and middle income countries. To lower the risk of hypertension, most physicians recommend drugs that have angiotensin-converting enzyme (ACE) inhibitors. However, prolonged use of such drugs is not recommended due to metabolic risks and the increase in the expression of ACE-II which could facilitate COVID-19 infection. In contrast, the intake of optimal macronutrients is one of the possible alternatives to naturally control hypertension. In the present study, a nontrivial feature selection and machine learning algorithm is adopted to intelligently predict the food-derived antihypertensive peptide. The proposed idea of the paper lies in reducing the computational power while retaining the performance of the support vector machine (SVM) by estimating the dominant pattern in the features space through feature filtering. The proposed feature filtering algorithm has reported a trade-off performance by reducing the chances of Type I error, which is desirable when recommending a dietary food to patients suffering from hypertension. The maximum achievable accuracy of the best performing SVM models through feature selection are 86.17% and 85.61%, respectively.<\/jats:p>","DOI":"10.3390\/nu14142794","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T07:51:56Z","timestamp":1657180316000},"page":"2794","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support Vector Machine"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8773-8726","authenticated-orcid":false,"given":"Mohammad Farhan","family":"Khan","sequence":"first","affiliation":[{"name":"Digby Stuart College, University of Roehampton, London SW15 5PU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gazal","family":"Kalyan","sequence":"additional","affiliation":[{"name":"Department of Pathology, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, ND 58202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sohom","family":"Chakrabarty","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4128-0427","authenticated-orcid":false,"given":"M.","family":"Mursaleen","sequence":"additional","affiliation":[{"name":"Department of Medical Research, China Medical University Hospital, China Medical University (Taiwan), Taichung 40402, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1016\/S0140-6736(20)30566-3","article-title":"Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study","volume":"395","author":"Zhou","year":"2020","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1001\/jama.2020.2648","article-title":"Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention","volume":"323","author":"Wu","year":"2020","journal-title":"JAMA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"E21","DOI":"10.1016\/S2213-2600(20)30116-8","article-title":"Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection","volume":"8","author":"Fang","year":"2020","journal-title":"Lancet Respir. 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