利用Apriori算法实现抑郁症病症表现的关联分析

Using Apriori Algorithm to Realize Association Analysis of Depression Symptoms

不同人群因不同的烦恼诸如工作压力大、学业压力大、经济不理想等会患有不同程度的抑郁症,对于大学生这个群体而言,抑郁症是最常见的精神障碍之一。抑郁大学生影响校园稳定的现象并不少见,而他们经常不清楚自己是否患有抑郁症。本文首先对收集到的抑郁症人群病症数据进行格式化处理,通过Apriori关联规则算法对其分析获得这些病症的频繁项集,设置最小支持度和置信度得到最终的强关联度规则模型。该方法方便了大学生进行抑郁症自测以进行及时的治疗或预防。

Different groups of people suffer from different degrees of depression due to different troubles,such as high work pressure,academic pressure,and poor economics,depression is one of the most common and harmful mental disorders among college students.It is not uncommon for depressed college students to affect campus stability,and they often do not know whether they are suffering from depression.This article first formatted the collected symptoms data of depression population,analyzed it through Apriori association rule algorithm to obtain the frequent itemsets of these diseases,set the minimum support and confidence to get the final strong association rule model.This method is convenient for college students to conduct self-test of depression for timely treatment or prevention.