论文标题
CHD风险通过生活方式控制最小化:机器学习网关
CHD Risk Minimization through Lifestyle Control: Machine Learning Gateway
论文作者
论文摘要
关于现代生活方式在临床冠心病(CHD)中的影响的研究主要集中在威慑健康因素上,例如吸烟,酒精摄入,奶酪消费和平均收缩压,在很大程度上不忽视健康生活方式在缓解冠心病风险中的影响。在这项研究中,使用了广泛的高级机器学习技术对30多年的世界卫生组织(WHO)数据进行了分析,以量化如何依赖对正健康指标的依赖,例如水果/蔬菜,谷物可以在一段时间内抵消CHD危险因素。我们的研究将负异常值对CHD的影响进行排名,然后量化积极健康因素在缓解负风险因素中的影响。我们的研究结果是通过简单的数学方程式提出的,仅使用生活方式控制概述了最佳的CHD预防策略。我们表明,SBP的摄入量增加了20%,降低了3-6%。或者,谷物摄入量增加了10%,SBP降低了3%;同时增加的水果蔬菜可以进一步抵消SBP的影响6%。我们的分析确立了在冠心病上的性别独立性,驳斥了长期以来的假设和不合格的信念。我们表明,可以随着生活方式和饮食的增量变化,例如果蔬菜摄入的摄入量的摄入量的饮食效果。我们的多元数据模型还估计了生活方式因素之间的功能关系,这些关系可能会重新定义基于Framingham得分的CHD预测的诊断。
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.