CHD Risk Minimization through Lifestyle Control: Machine Learning Gateway.

He, Xi and Matam, B Rajeswari and Bellary, Srikanth and Ghosh, Goutam and Chattopadhyay, Amit K (2020) CHD Risk Minimization through Lifestyle Control: Machine Learning Gateway. Scientific reports, 10 (1). p. 4090. ISSN 2045-2322. This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs

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Abstract

Studies on the influence of a modern lifestyle in abetting Coronary Heart Diseases (CHD) have mostly focused on deterrent health factors, like smoking, alcohol intake, cheese consumption and average systolic blood pressure, largely disregarding the impact of a healthy lifestyle in mitigating CHD risk. In this study, 30+ years' World Health Organization (WHO) data have been analyzed, using a wide array of advanced Machine Learning techniques, to quantify how regulated reliance on positive health indicators, e.g. fruits/vegetables, cereals can offset CHD risk factors over a period of time. Our research ranks the impact of the negative outliers on CHD and then quantifies the impact of the positive health factors in mitigating the negative risk-factors. Our research outcomes, presented through simple mathematical equations, outline the best CHD prevention strategy using lifestyle control only. We show that a 20% increase in the intake of fruit/vegetable leads to 3-6% decrease in SBP; or, a 10% increase in cereal intake lowers SBP by 3%; a simultaneous increase of 10% in fruit-vegetable can further offset the effects of SBP by 6%. Our analysis establishes gender independence of lifestyle on CHD, refuting long held assumptions and unqualified beliefs. We show that CHD risk can be lowered with incremental changes in lifestyle and diet, e.g. fruit-vegetable intake ameliorating effects of alcohol-smoking-fatty food. Our multivariate data model also estimates functional relationships amongst lifestyle factors that can potentially redefine the diagnostics of Framingham score-based CHD-prediction.

Item Type: Article
Additional Information: This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs
Subjects: QT Human physiology
QY Clinical pathology
WG Cardiovascular system. Cardiology
Divisions: Emergency Services > Cardiology
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Depositing User: Jamie Edgar
Date Deposited: 13 Mar 2020 12:35
Last Modified: 13 Mar 2020 12:35
URI: http://www.repository.uhblibrary.co.uk/id/eprint/2920

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