Application of Negative and Positive Association Rules in Mental Health Analysis of College Students

College students are suffering from many mental health problems including somatization, obsessive, interpersonal sensitivity, depression, anxiety, hostility, fear, paranoia and psychosis, which can bring a lot of negative effects to them. Many association rules mining algorithms have been used to analyze the relationships between those problems from mental health datasets. However, they only focus on positive association rules (PARs) and don't consider negative association rules (NARs), which can provide much more informative information than the positive ones. So this paper aims to mine both positive and negative association rules (PNARs) from mental health datasets of college students. The form of NARs like a1→a2⇒1r→b2 we mined contains both positive and negative items in each side, but in traditional forms of NARs, it contains all positive items or all negative items in each side like ala2⇒(b1b2) or →(a1a2)⇒b1b2. We first use e-NFIS algorithm to mine positive frequent itemsets (PFIS) and negative frequent itemsets (NFIS) and then use the support-confidence framework to generate the PARs and NARs based on the obtained PFIS and NFIS respectively. The real dataset is collected form 2275 freshmen's symptom self-rating scale (SCL _90) test from one Chinese college. Experimental results verify our approach can easily find PNARs between the various mental problems, and the obtained rules have practical guiding significance for predicting and preventing the mental health of college students.

[1]  Tutut Herawan,et al.  Mining Interesting Association Rules of Students Suffering Study Anxieties Using SLP-Growth Algorithm , 2012, Int. J. Knowl. Syst. Sci..

[2]  Jenny K Hyun,et al.  Graduate Student Mental Health: Needs Assessment and Utilization of Counseling Services , 2006 .

[3]  Lu Yuchang,et al.  Study on Negative Association Rules , 2004 .

[4]  Xindong Wu,et al.  Efficient mining of both positive and negative association rules , 2004, TOIS.

[5]  Das Amrita,et al.  Mining Association Rules between Sets of Items in Large Databases , 2013 .

[6]  Xiangjun Dong,et al.  Positive and Negative Association Rules Mining for Mental Health Analysis of College Students , 2017 .

[7]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[8]  B. Prabhakaran,et al.  Association Rule Mining in Multiple, Multidimensional Time Series Medical Data , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[9]  Huang Shu-chen Statistical Analysis and Association Rule Mining of Application in College Students' mental Health , 2014 .

[10]  Qingfang Meng,et al.  Tree-based frequent itemsets mining for analysis of life-satisfaction and loneliness of retired athletes , 2017, Cluster Computing.

[11]  Xiong Zhong-yang Improved algorithm of mining association rules with negative items , 2008 .

[12]  Jiang Hong-bo,et al.  Application Research on Fast Discovery of Association Rules Based on Air Transportation , 2007, 2007 International Conference on Service Systems and Service Management.

[13]  Xiangjun Dong,et al.  e-NFIS: Efficient negative frequent itemsets mining only based on positive ones , 2011, 2011 IEEE 3rd International Conference on Communication Software and Networks.

[14]  T. Wilens,et al.  College Students: Mental Health Problems and Treatment Considerations , 2015, Academic Psychiatry.

[15]  Shivendra Jena,et al.  Stress and mental health problems in 1st year medical students: a survey of two medical colleges in Kanpur, India - , 2015 .

[16]  He Jiang,et al.  Application of multidimensional association rules in personal financial services , 2010, 2010 International Conference On Computer Design and Applications.

[17]  H. Koh,et al.  Data mining applications in healthcare. , 2005, Journal of healthcare information management : JHIM.

[18]  Cai Kangxu Construction of Fuzzy Evaluation Index System of College Student's Psychological Health Education , 2012 .

[19]  Ren Ping Improvement of Apriori Algorithm Based on Association Rules and the Application in the Insurance CRM of China , 2009 .