Proposing a new methodology on vague association rule mining for the diagnosis of heart disease hesitation patterns

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    In the realistic situation, the health care has which contain imprecisely specified data. This imprecise data indicates the presence of vagueness, incompleteness and uncertainty which causes the problem during important decision-making task in the prediction of heart disease. Traditional Association Rule Mining has limitations as it only deals with the features that are actually present in the prediction of heart disease and ignores the features that are almost not considered for the heart disease prediction. Furthermore, these features may be placed with predictive feature by imposing its attractiveness measure; disease prediction pattern mining in the scenario of imprecise and vague environment is very difficult which is frequent in recent years. For the effectiveness of retrieved hesitated patterns and rules, the concept of vague set theory is used. For the same consideration, heart disease dataset features as weighting factor is used for generation of disease prediction patterns.

     

     

     

  • Keywords


    Heart Disease, Vague Set Theory; Disease Prediction Pattern; Weighting Factor; Attractiveness Measure; Hesitant Pattern; Association Rule Mining.

  • References


      [1] Patel, Vimla L., et al. "The coming of age of artificial intelligence in medicine." Artificial intelligence in medicine 46.1 (2009): 5-17. https://doi.org/10.1016/j.artmed.2008.07.017.

      [2] Wu, Xianguo, et al. "A hybrid information fusion approach to safety risk perception using sensor data under uncertainty." Stochastic Environmental Research and Risk Assessment32.1 (2018): 105-122. https://doi.org/10.1007/s00477-017-1389-9.

      [3] Thiagarasu, P. Umasankar V. "Mining Correlation Rules for Interval-Vague Sets." International Journal Of Engineering And Computer Science 6.2 (2017).

      [4] Thiagarasu, V., and P. Umasankar. "Mining Correlation Rules for Multiple Attribute Group Decision Making Models with Vague Sets." International Journal of Applied Engineering Research11.16 (2016): 8848-8857.

      [5] V. Thiagarasu, P. Umasankar. “Mining MAGDM Problems with Triangular Vague Sets” International Journal of Scientific and Research Publications, 11.16 (2016): 297-310.

      [6] Durairaj M, Poornappriya T S, “Survey on Vague Set Theory for Decision Making in Various Applications”, International Journal of Emerging Technology and Advanced Engineering, Volume 8, Special Issue 2, pp. 104-107, 2018.

      [7] http://archive.ics.uci.edu/ml/datasets/heart+disease

      [8] Bhatt, Anurag, Sanjay Kumar Dubey, and Ashutosh Kumar Bhatt. "Analytical Study on Cardiovascular Health Issues Prediction Using Decision Model-Based Predictive Analytic Techniques." Soft Computing: Theories and Applications. Springer, Singapore, 2018. 289-299. https://doi.org/10.1007/978-981-10-5699-4_28.

      [9] LakshmanaprabuS.K, Sachi Nandan Mohanty, K. Shankar, Arunkumar N, GustavoRamireze. “Optimal deep learning model for classification of lung cancer on CT images”, Future Generation Computer Systems. 2018. https://doi.org/10.1016/j.future.2018.10.009.

      [10] Lakshmanaprabu SK, K. Shankar, Deepak Gupta, Ashish Khanna, Joel J. P. C. Rodrigues, Plácido R. Pinheiro, Victor Hugo C. de Albuquerque. “Ranking Analysis for Online Customer Reviews of Products Using Opinion Mining with Clustering”. Complexity, 2018: 1-9. https://doi.org/10.1155/2018/3569351.

      [11] K. Shankar, Lakshmanaprabu S.K, Deepak Gupta, Andino Maseleno, Victor Hugo C. de Albuquerque. Optimal Features Based Multi Kernel SVM Approach for Thyroid Disease Classification. The Journal of Supercomputing, 2018. https://doi.org/10.1007/s11227-018-2469-4.

      [12] Lakshmanaprabu SK, K. Shankar, Ashish Khanna, Deepak Gupta, Joel J. P. C. Rodrigues, Plácido R. Pinheiro, Victor Hugo C. de Albuquerque. Effective Features to Classify Big Data using Social Internet of Things. IEEE Access, 6 (2018): 24196-24204. https://doi.org/10.1109/ACCESS.2018.2830651.

      [13] Andino Maseleno, Alicia Y.C. Tang, Moamin A. Mahmoud, Marini Othman, Suntiaji Yudo Negoro, Soukaina Boukri, K. Shankar, Satria Abadi, Muhamad Muslihudin. “The Application of Decision Support System by Using Fuzzy Saw Method in Determining the Feasibility of Electrical Installations in Customer’s House”. International Journal of Pure and Applied Mathematics, 119. 16 (2018): 4277-4286.

      [14] Muhammad Muslihudin, Risma Wanti, Hardono, Nurfaizal, K. Shankar, Ilayaraja M, Andino Maseleno, Fauzi, Dwi Rohmadi Mustofa, Muhammad Masrur, Siti Mukodimah, “Prediction of Layer Chicken Disease using Fuzzy Analytical Hierarcy Process”, International Journal of Engineering & Technology, 7. 2.26 (2018): 90-94.

      [15] Eka Sugiyarti, Kamarul Azmi Jasmi, Bushrah Basiron, Miftachul Huda, K. Shankar, Andino Maseleno, “Decision Support System of Scholarship Grantee Selection using Data Mining”, International Journal of Pure and Applied Mathematics, 119.15 (2018): 2239-2249.

      [16] Tri Susilowati, Kamarul Azmi Jasmi, Bushrah Basiron, Miftachul Huda, K. Shankar, Andino Maseleno, Anis Julia, Sucipto, “Determination of Scholarship Recipients using Simple Additive Weighting Method”, International Journal of Pure and Applied Mathematics, 119. 15 (2018): 2231-2238.

      [17] K. Shankar. “Prediction of Most Risk Factors in Hepatitis Disease using Apriori Algorithm”, Research Journal of Pharmaceutical, Biological and Chemical Sciences, 8. 5 (2017): 477-484.

      [18] Nur Aminudin, Eni Sundari, K. Shankar, P. Deepalakshmi, Fauzi, Rita Irviani, Andino Maseleno. “Weighted Product and Its Application to Measure Employee Performance”, International Journal of Engineering & Technology, 7. 2.26 (2018): 102-108. https://doi.org/10.14419/ijet.v7i2.27.11574.


 

View

Download

Article ID: 14874
 
DOI: 10.14419/ijet.v7i4.14874




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.