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Discovery of Strong Association Rules for Attributes from Data for Program of All-Inclusive Care for the Elderly (PACE)

机译:从老年人全包照顾计划(PACE)的数据中发现属性的强关联规则

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The Program of All-inclusive Care for the Elderly (PACE) (2013)[6] study aimed to find out if the program we designed for the 11 month treatment can efficiently help people lose weight, and even can keep tracking of weight loss and body fat by checking some of the parameters we measured during the 11 months. We worked on the potentially significant parameters for weight loss in 11 months, such as age, height, weight, body size and body fat. We used association rule mining and classification rule mining to discover which parameters are significant for weight loss and what are the associations between weight loss and those significant parameters. Experimental results showed that weight loss with support from 0.2 to 0.9 and confidence from 0.7 to 1.0 is related to body weight and the changes of chest size, arm size, waist size, thigh size and hip size. In future, we will discover the associations among body weight, body size, body fat, heart beat and blood pressure.
机译:老年人全包护理计划(PACE)(2013)[6]旨在研究我们为11个月治疗设计的计划是否可以有效地帮助人们减轻体重,甚至可以跟踪体重减轻和通过检查我们在11个月内测得的一些参数来确定体内脂肪。我们研究了11个月内体重减轻的潜在重要参数,例如年龄,身高,体重,体重和体脂。我们使用关联规则挖掘和分类规则挖掘来发现哪些参数对减肥有效,以及减肥与那些重要参数之间的关联是什么。实验结果表明,在支持度为0.2至0.9和置信度为0.7至1.0的情况下,体重减轻与体重以及胸部大小,手臂大小,腰围大小,大腿大小和臀部大小的变化有关。将来,我们将发现体重,体重,体脂,心跳和血压之间的关联。

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