Determination of factors related to coronary heart diseases by associative classification technique

Year 2021, Volume: 7 Issue: 3, 360 - 365, 31.12.2021

Zeynep Küçükakçalı , İpek Balıkçı Çiçek

https://doi.org/10.19127/mbsjohs.1004917

Abstract

Objective: The goal of this study is to categorize CHD using the relational classification approach on a CHD dataset made up of open access patients with and without CHD, as well as to disclose the disease's relationship to the risk factors that cause CHD.
Methods: The associative classification model was applied to the open-access data set “CHD” in this study. The performance of the model was evaluated by accuracy, specificity, negative predictive value. According to the results of the associative classification model, the factors associated with the disease were determined by specific rules. groups, examined using Mann-Whitney U, Pearson Chi-square test, and Fisher's Exact test. p<0.05 values were considered statistically significant.
Results: For the associative classification model applied to the data set, the results of the performance metrics that specificity, accuracy, and negative predictive value were calculated as 0.995, 0.852, 0.854, respectively.
Conclusion: The conclusions of this investigation revealed that the study conducted on the CHD data set with the associative classification model yielded successful results. Since the results obtained from the associative classification model reveal certain rules, it is very easy for users to understand and the results can be easily interpreted. Thus, the findings obtained with this model can be used quite easily in preventive medicine practices.

Keywords

CHD, classification, association rules, associative classification.

References

• 1. Gunes Z. Social Support and States of Hopelessness Perceived by Individuals with Chronic Diseases from the Family. Florence Nightingale Journal of Nursing. 2009;17(1):24-31.
• 2. Kilic M. Primary approach to the prevention of chronic diseases: Screening tests. Turkish Journal of Family Practice/ Turkish Journal of Family Medicine. 2011;15(2).
• 3. Organization WH. Noncommunicable diseases country profiles 2018. 2018. 4. Durusoy E, Yildirim T, Altun A. Outpatient follow-up of coronary artery disease. Trakya Univ Medical Faculty Journal. 2010;27(1):13-8.
• 5. Cihan S, Karabulut B, Arslan G, Cihan G. Examination of the risk of coronary artery disease with data mining methods. International Journal of Engineering Research and Development. 2018;10(1):85-93.
• 6. Oguz S, Cesur K, Koc S. Coronary Heart Disease Risk Factors in the Determination of Nursing Students. Turk Soc Cardiol Turkish Journal of Cardiovascular Nursing. 2011;2(2):18-21
• 7. Silahtaroglu G. Basic Data Mining with Concepts and Algorithms Papatya Publishing Education Inc. Istanbul, Turkey. 2008.
• 8. Witten IH, Frank E. Data mining: practical machine learning tools and techniques with Java implementations. Acm Sigmod Record. 2002;31(1):76-7.
• 9. Chen Y-L, Chen J-M, Tung C-W. A data mining approach for retail knowledge discovery with consideration of the effect of shelf-space adjacency on sales. Decision support systems. 2006;42(3):1503-20.
• 10. Vinodh S, Prakash NH, Selvan KE. Evaluation of leanness using fuzzy association rules mining. The International Journal of Advanced Manufacturing Technology. 2011;57(1-4):343-52.
• 11. Thabtah FA. A review of associative classification mining. Knowledge Engineering Review. 2007;22(1):37-65.
• 12. Kumar AS, Wahidabanu R, editors. A frequent item graph approach for discovering frequent itemsets. 2008 International Conference on Advanced Computer Theory and Engineering; 2008: IEEE.
• 13. Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R, editors. Advances in knowledge discovery and data mining1996: American Association for Artificial Intelligence.
• 14. Allender S, Peto V, Scarborough P, Boxer A, Rayner M. Coronary heart disease statistics. 2007.
• 15. Sanchis-Gomar F, Perez-Quilis C, Leischik R, Lucia A. Epidemiology of coronary heart disease and acute coronary syndrome. Annals of translational medicine. 2016;4(13).
• 16. Percin I, Yagin FH, Guldogan E, Yologlu S, editors. ARM: An Interactive Web Software for Association Rules Mining and an Application in Medicine. 2019 International Artificial Intelligence and Data Processing Symposium (IDAP); 2019: IEEE. 17. Thabtah F. A review of associative classification mining. The Knowledge Engineering Review. 2007;22(1):37-65.
• 18. Jabbar MA, Deekshatulu BL, Chandra P, editors. Heart disease prediction using lazy associative classification. 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s); 2013: IEEE.
• 19. Park H-Y, Lim D-J. A design failure pre-alarming system using score-and vote-based associative classification. Expert Systems with Applications. 2021;164:113950.
• 20. Yao X, Pei X, Yang Y, Zhang H, Xia M, Huang R, et al. Distribution of diabetic retinopathy in diabetes mellitus patients and its association rules with other eye diseases. Scientific Reports. 2021;11(1):1-10.