Studies on the influence of a modern lifestyle in abetting Coronary Heart Diseases (CHD) have mostly focused on deterrent health factors, likesmoking, 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.Globally, cardiovascular diseases account for nearly 17.9 million deaths with Coronary Heart Disease (CHD) accounting for 80% of these 1 . A myriad of factors have been identified as risk generators, including ethnicity, sex, total cholesterol level, triglycerides, blood pressure, that in turn are affected by life style denominators 2,3 . Together, they determine the risk appraisal function that have been assessed using conventional predictive scoring like body mass index (BMI) and Framingham scores 4,5 together with more advanced population biology or epidemiological estimators. Although there have been numerous advances in the treatment of established CHD, at a population level, assessed through Artificial Intelligence (Machine Learning) based adaptation of established statistical wisdom, remains a major knowledge gap 5 .Ground breaking epidemiological studies have identified key lifestyle and health indicators as risk factors for Coronary Heart Disease (CHD) 6-9 . Lifestyle factors include smoking 10,11 , alcohol consumption 12 , lack of physical activity while key health indicators include obesity, high blood pressure 5 and diabetes 13 . Evidence suggests a diet rich in fruits, vegetables and whole grains can mitigate the onset of CHD 8 . Some of these risk factors are not individually causative but when combined with other risk factors, increase the risk of CHD 10-14 . Further, lifestyle factors can be suitably modified to ameli...