高斯判别模型对人类心脏疾病的预测分析

Predictive Analysis of Gauss Discriminant Model for Human Heart Disease

随着现代化生活方式普及,大部分劳动者利用个人电脑进行办公,长时间坐着办公减少了必要的身体锻炼容易引发心脏疾病。本文从个人身体信息出发,利用分类预测模型建立对个人心脏疾病的预警机制。对于个体的年龄、性别、胸痛类型、静息血压等分类数值以及胆固醇含量、静息血压、最大心率等连续型数值进行描述性统计分析。区别逻辑回归等判别式学习算法,另辟蹊径。从贝叶斯先验角度出发引入了生成模型中推导严谨的高斯判别模型。

With the spread of modern lifestyles and the use of personal computers for office work, sitting for long periods of time reduces the need for physical exercise and can lead to heart disease. In this paper, based on personal body information, classification prediction model is used to establish an early warning mechanism for individual heart disease. Descriptive statistical analysis was performed on age, sex, type of chest pain, resting blood pressure and continuous values such as cholesterol content, resting blood pressure and maximum heart rate. Discriminant learning algorithms, such as logistic regression, provide a new way. From the perspective of Bayesian prior, a rigorous Gaussian discriminant model is introduced in the generation model.