Developing an Expert System Application to Detect Childs' Lung Disease

Authors

  • Sulis Sandiwarno  Department of Computer Science, Universitas Mercu Buana University, Indonesia

DOI:

https://doi.org/10.32628/CSEIT206657

Keywords:

Artificial intelligence, NB, SVM, Lungs

Abstract

The development of information technology has supported many activities, especially in terms of health. Artificial Intelligence (AI) is the application of information technology that is currently developing well. Several previous studies have evaluated models from expert systems to diagnose lung disease in children using Naïve Bayes (NB) and Support Vector Machine (SVM). However, in conducting these evaluations they do not try to make an integrated application to facilitate evaluation. In this study we propose to build a system that integrates NB and SVM classifiers. Furthermore, in this study we used a sample of data from a clinic in Indonesia. The results of this study, we conclude that the existence of this system will make it easier to evaluate the lung disease experienced by children.

References

  1. Sadikin M, Fanany MI, Basaruddin T (2016) A New Data Representation Based on Training Data Characteristics to Extract Drug Name Entity in Medical Text. Comput Intell Neurosci. https://doi.org/10.1155/2016/3483528
  2. Sadikin M (2017) Mining relation extraction based on pattern learning approach. Indones J Electr Eng Comput Sci. https://doi.org/10.11591/ijeecs.v6.i1.pp50-57
  3. Triana YS (2018) Monte Carlo Simulation for Modified Parametric of Sample Selection Models Through Fuzzy Approach. In: IOP Conference Series: Materials Science and Engineering
  4. Kurniawan R, Yanti N, Ahmad Nazri MZ, Zulvandri (2015) Expert systems for self-diagnosing of eye diseases using Naïve Bayes. In: Proceedings - 2014 International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2014
  5. de Carvalho Filho AO, Silva AC, de Paiva AC, et al (2017) Lung-Nodule Classification Based on Computed Tomography Using Taxonomic Diversity Indexes and an SVM. J Signal Process Syst. https://doi.org/10.1007/s11265-016-1134-5
  6. Naqi SM, Sharif M, Yasmin M (2018) Multistage segmentation model and SVM-ensemble for precise lung nodule detection. Int J Comput Assist Radiol Surg. https://doi.org/10.1007/s11548-018-1715-9
  7. Wang S, Jiang L, Li C (2015) Adapting naive Bayes tree for text classification. Knowl Inf Syst 44:77–89. https://doi.org/10.1007/s10115-014-0746-y
  8. Balamurugan AA, Rajaram R, Pramala S, et al (2011) NB+: An improved Naïve Bayesian algorithm. Knowledge-Based Syst 24:563–569. https://doi.org/10.1016/j.knosys.2010.09.007
  9. Jiang L, Zhang L, Yu L, Wang D (2019) Class-specific attribute weighted naive Bayes. Pattern Recognit 88:321–330. https://doi.org/10.1016/j.patcog.2018.11.032
  10. Huang H, Wei X, Zhou Y (2018) Twin support vector machines: A survey. Neurocomputing 300:34–43. https://doi.org/10.1016/j.neucom.2018.01.093
  11. Weston J, Watkins C (1999) Support Vector Machines for Multi-Class Pattern Recognition. Proc 7th Eur Symp Artif Neural Networks
  12. Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Networks 13:415–425. https://doi.org/10.1109/72.991427
  13. He X, Wang Z, Jin C, et al (2012) A simplified multi-class support vector machine with reduced dual optimization. Pattern Recognit Lett 33:71–82. https://doi.org/10.1016/j.patrec.2011.09.035

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Published

2020-12-30

Issue

Section

Research Articles

How to Cite

[1]
Sulis Sandiwarno, " Developing an Expert System Application to Detect Childs' Lung Disease" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 6, pp.285-290, November-December-2020. Available at doi : https://doi.org/10.32628/CSEIT206657