Applications of Soft Computing Techniques for Pulmonary Tuberculosis Diagnosis
Siraj Sebhatu1, Ashok Kumar Sahoo2, Pooja3

1Siraj Sebhatu, Ph.D. Student, Department of Computer Science and Engineering, School of Engineering & Technology Sharda University, Noida, (Uttar Pradesh), India.
2Ashok Kumar Sahoo, Associate Professor, Department of Computer Science & Engineering, Sharda University, Noida (Uttar Pradesh), India.
3Pooja, Ph.D, Department of Computer Science and Engineering, School of Engineering & Technology Sharda University, Noida, (Uttar Pradesh), India.
Manuscript received on 15 November 2019 | Revised Manuscript received on 04 December 2019 | Manuscript Published on 10 December 2019 | PP: 119-127 | Volume-8 Issue-3S2 October 2019 | Retrieval Number: C10201083S219/2019©BEIESP | DOI: 10.35940/ijrte.C1020.1083S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Recently, several interesting research studies have been reported on soft computing approaches. Soft computing approaches are solving several kinds of problems and provide alternative solutions. Different Soft computing techniques or approaches have been applied in medical care data for effective diagnosis prediction. Those approaches implemented on diseases diagnosing of pulmonary tuberculosis and obtaining better results in comparison to traditional approaches. This approach is an aggregation of methodologies that were combined various model and provide solutions to those problems that are difficult to handle in real-world situations. Researchers keep developing of an accurate and reliable intelligent decision-making method for the construction of pulmonary tuberculosis diagnosis system. The existing diagnostic testing system procedures are not only tedious, they also take a long time to analyze. Therefore, the diagnosis of tuberculosis still requires further improvements to new rapid and accurate diagnostic model and techniques that enable higher sensitivity and specificity to be achieved, thus promoting disease control and Prevention. State of the art makes approaches to soft computing more powerful, more reliable and more efficient. The importance of this review paper is to distinguish the different soft computing approaches used to support pulmonary tuberculosis disease diagnosis, identification, prediction and intelligent classification. In the field, researchers and medical practitioners look forward to using approaches to soft computing. Some of these are an artificial neural network, genetic algorithm, and support vector machine, fuzzy logic etc. latest methods in the diagnostic field uses artificial neural network. Some of the other benefits of Artificial neural network is an easy – to – optimize, resources and adoptable non – linear modeling of expansive data sets and predictive inference accuracy demonstrating that artificial neural network could serve as a valuable decision support tool in various fields, including medicine.
Keywords: Artificial Neural Network, Fuzzy and Fuzzy Logic, Genetic Algorithm, Pulmonary Tuberculosis, Support Vector Machine.
Scope of the Article: Soft computing Signal and Speech Processing