Total views : 297
Alcoholic Behavior Prediction through Comparative Analysis of J48 and Random Tree Classification Algorithms using WEKA
Objectives/Background: Addiction of alcohol is a complex disease which results from diversity of social, genetic and environmental influences. A report by World Health Organization, WHO (2014) estimates that most of the deaths are from alcohol related causes.The objective of this study is to analyze the alcoholic behavior of different age group people on the basis of risk factors. In this paper, we construct a comparative model of different classification techniques to analyze the best algorithm for predicting the alcoholic behavior of a person. Methods: Under this context, random tree and J48 that are decision tree algorithms have been exercised on the dataset of 600 people that is collected through a structured questionnaire by visiting de addicted centers, colleges, villages, government offices, old age homes of Patiala, Punjab. Findings: Results conclude that the random tree provides more precise results than J48 for all the age group people. Risk factors that come out to be most effective are impulsive nature, sensation seeking nature, financial loss, family conflict, depression, child abuse, alcoholic shop near home distance.The overall accuracy of random tree is 75.94% and for J48 is 71.26%. Applications/Improvement: There is a need to develop some intelligent tools in this area and the rules extracted from this analysis can be further used for designing the tool. More attributescan be incorporated to achieve the optimal results for predicting the behavior of an alcoholic person.
Addiction, Classification, Data Mining, Prediction.
- Maggs JL, Schulenberg JE. Trajectories of alcohol use during the transition to adulthood. Alcohol Research and Health. 2004 Dec; 28(4):195–201.
- McGue M, Iacono WG. The association of early adolescent problem behavior with adult psychopathology. American Journal of Psychiatry. 2005 Jun; 162(6):1118-1124.
- Williams PS, Hine DW. Parental behavior and alcohol misuse among adolescents: A path analysis or mediating influences. Australian Journal of Psychology. 2002 Apr; 54(1):17-24.
- World Health Organization (WHO). Global status report on alcohol and health. http://www.who.int/substance_abuse/publications/global_alcohol_report/en/. Date Accessed: 2014.
- Organization for Economic Cooperation and Development (OECD). http://www.oecd.org/health/tackling-harmful-alcohol-use-9789264181069-en.htm. Date Accessed: 12/05/2015.
- Silver M, Sakara T, Su HC, Herman C, Dolins SB, O’shea MJ. Case study: how to apply data mining techniques in a healthcare data warehouse. Health care Information Management. 2001 Feb; 15(2):155-164.
- Bellazzi R, Zupan B. Predictive data mining in clinical medicine: current issues and guidelines. International Journal of Medicine Information. 2008 Feb; 77(2):81-97.
- Natalie Guillen, Erick Rotha, Alhena Alfaroa, Erik Fernandez. Youth alcohol drinking behavior: Associated risk and protective factors. Revista Iberoamericana de Psicologia Y Salud. 2015 Jul; 6(2):53-63.
- Jennifer Mendel R, Carla Berg J, Rebecca Windle C, Michael Windle. Predicting young adulthood smoking among adolescent smokers and nonsmokers. American Journal of Health Behavior. 2012 Jul; 36(4):542–554.
- Brett Maclennan, Kypros Kypria, John Langleya, Robin Room. Non-response bias in a community survey of drinking, alcohol-related experiences and public opinion on alcohol policy. Drug and Alcohol Dependence. 2012 Nov; 126(1-2):189-194.
- Marie Yap, Anthony Jorm, Renee Bazley, Claire Kelly, Siobhan Ryan, Dan Lubman. Web-Based Parenting Program to Prevent Adolescent Alcohol Misuse: Rationale and Development. Australas Psychiatry. 2011 Aug; 19(4):339-344.
- Ryan SM, Jorm AF, Lubman DI. Parenting factors associated with reduced adolescent alcohol use: A systematic review of longitudinal studies. Australian and New Zeeland Journal of Psychiatry. 2010 Sep; 44(9):774-783.
- Cable N, Sacker A. Typologies of alcohol consumption in adolescence: Predictors and adult outcomes. Alcohol and Alcoholism. 2008 Jan-Feb; 43(1):81–90.
- Katrijn Houben, Klaus Rothermund, Reinout Wiers W. Predicting alcohol use with a recoding-free variant of the Implicit Association Test. Addictive Behaviors. 2009 Jan; 34(5):487-489.
- Hamburger ME, Leeb RT, Swahn MH. Childhood maltreatment and early alcohol Use among high-risk adolescents. Journal of Studies on Alcohol and Drugs. 2008 Mar; 69(2):291-295.
- Hiro H, Kawakami N, Tanaka K, Nakamura K. Association between job stressors and heavy drinking: age differences in male Japanese workers. Japan Work Stress and Health Cohort Study Group. 2007 Jun; 45(3):415-425.
- Legault L, Anawati M, Flynn R. Factors favoring psychological resilience among fostered young people. Children and Youth Services Review. 2006 Sep; 28(9):1024-1038.
- White HR, Jackson K. Social and psychological influences on emerging adult drinking behavior. Alcohol Research and Health. 2004 Dec; 28(4):182-190.
- Anuradha C, Velmurugan T. A comparative analysis on the evaluation of classification algorithms in the prediction of students’ performance. Indian Journal of Science and Technology. 2015 Jul; 8(15):1-12.
- Tripti Mishra, Dharminder Kumar, Sangeeta Gupta. Mining students’ data for performance prediction. Fourth International Conference on Advanced Computing and Communication Technologies. 2014 Feb, 255-262.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.