Shesop Healthcare：使用支持向量机内核进行压力和流感分类
Shesop Healthcare: Stress and influenza classification using support vector machine kernel
摘要：
Shesop是一个综合系统，使人类的生活更轻松，并在医疗保健方面帮助人们。压力和流感分类是Shesop应用于智能手表，Polar和Fitbit等医疗设备的一部分。本文的主要目的是对一个新的数据进行分类，并告知你是否有压力，抑郁，流感。我们将使用在万隆几个月的心率数据，分析数据，并找到与压力和流感水平不断相关的心率变化。在我们找到变量之后，我们将使用该变量作为支持向量机学习的输入。我们将使用拉格朗日和核技术将2D数据转换为3D数据，以便我们可以在3D空间中使用线性分类。最后，我们可以使用机器学习的结果对新数据进行分类，并立即得到最终结果：压力或流感。
英文摘要：
Shesop is an integrated system to make human lives more easily and to help people in terms of healthcare. Stress and influenza classification is a part of Shesop's application for a healthcare devices such as smartwatch, polar and fitbit. The main objective of this paper is to classify a new data and inform whether you are stress, depressed, caught by influenza or not. We will use the heart rate data taken for months in Bandung, analyze the data and find the Heart rate variance that constantly related with the stress and flu level. After we found the variable, we will use the variable as an input to the support vector machine learning. We will use the lagrangian and kernel technique to transform 2D data into 3D data so we can use the linear classification in 3D space. In the end, we could use the machine learning's result to classify new data and get the final result immediately: stress or not, influenza or not.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
