Abstract
The problem of classification in medical data has been well studied and there exist number of solution for the classification of medical using different measures and methods. However, the methods still suffer to achieve higher performance in classification accuracy. Towards the development of classification performance, the Multi Level Incremental Influence Measure (MLIIM) based classification algorithm is presented in this paper. The method preprocess the input data set to fix the noise issue by removing the incomplete data. In the second stage, the method estimates the influence measure in multiple levels at iterative manner. Finally, the method estimates, class influence weight (CIW) for different classes. Based on the computed class influence weight, an target class is selected to assign label to the data point. The proposed algorithm produces efficient classification and increases the classification accuracy.
Similar content being viewed by others
References
Ansarullah, S.I.: Heart disease prediction system using data mining techniques. Int. Res. J. Eng. Technol. 56(3), 735–760 (2016)
Kirmani, M.M.: Cardiovascular disease prediction using data mining techniques. Orient. J. Comput. Sci. Technol. (2016). https://doi.org/10.13005/ojcst/10.02.38
Purusothaman, G., Krishnakumari, P.: A survey of data mining techniques on risk prediction: Heart disease. Int. J. Sci. Technol. 8(2), 1 (2015)
Santhanam, T.: Heart disease prediction using hybrid genetic fuzzy model. Int. J. Sci. Technol. 8(15), 797 (2015)
Kavakiotisa, I.: Machine learning and data mining methods in diabetes research. J. Comput. Struct. Biotechnol. 15, 104–116 (2017)
Kavakiotis, I., Tzanis, G., Vlahavas, I.: Mining frequent patterns and association rules from biological data. Comput. Tech. Eng. Ch. 34, 735–760 (2014)
Worachartcheewan, A., Nantasenamat, C., Isarankura-Na-Ayudhya, C., Prachayasittikul, V.: Quantitative population–health relationship (QPHR) for assessing metabolic syndrome. EXCLI J. 12, 569–583 (2013)
Lan, W., Wang, J., Li, M., Liu, J., Wu, F.X., Pan, Y.: Predicting microRNA-disease associations based on improved microRNA and disease similarities. IEEE/ACM Trans. Comput. Biol. Bioinf. (2016). https://doi.org/10.1109/TCBB.2016.2586190
Sathishkumar, E.N., Thangavel, K., Chandrasekha, T.: A novel approach for single gene selection using clustering and dimensionality reduction. Int. J. Sci. Eng. Res. 4(5), 1540–1545 (2013)
Maji, P., Das, C.: Relevant and significant supervised gene clusters for microarray cancer classification. IEEE Trans. Nanobiosci. 11(2), 161–168 (2012)
Maulik, U., Mukhopadhyay, A., Chakraborty, D.: Gene-expression-based cancer subtypes prediction through feature selection and transductive SVM. IEEE Trans. Biomed. Eng. 60(4), 1111–1117 (2013)
Kawano, S., et al.: Identifying gene pathways associated with cancer characteristics via sparse statistical methods. IEEE/ACM Trans. Comput. Biol. Bioinf. 9(4), 966–972 (2012)
Li, J., Su, H., Chen, H., Futscher, B.W.: Optimal search-based gene subset selection for gene array cancer classification. IEEE Trans. Inf. Technol. Biomed. 11(4), 398–405 (2007)
Tsai, C.-A., et al.: Gene selection for sample classifications in microarray experiments. DNA Cell Biol. 23(10), 607–614 (2004)
Vijayakumar, K., Arun, C.: Analysis and selection of risk assessment frameworks for cloud based enterprise applications. Biomed. Res. (2017). ISSN: 0976-1683 (Electronic)
Vijayakumar, K.: Arun, C: Continuous security assessment of cloud based applications using distributed hashing algorithm in SDLC. Cluster Comput. (2017). https://doi.org/10.1007/s10586-017-1176-x
Vijayakumar, K., Arun, C.: Automated risk identification using NLP in cloud based development environments. J. Ambient Intell. Hum. Comput. (2017). https://doi.org/10.1007/s12652-017-0503-7
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ananthajothi, K., Subramaniam, M. Multi level incremental influence measure based classification of medical data for improved classification. Cluster Comput 22 (Suppl 6), 15073–15080 (2019). https://doi.org/10.1007/s10586-018-2498-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-018-2498-z