Differential Diagnosis of Tuberculosis and Pneumonia using Machine Learning
Aiyesha Sadiya1, Anusha V Illur2, Aekhata Nanda3, Eshwar Rao4, Vidyashree K P5, Mansoor Ahmed6
1Aiyesha Sadiya, Department of Information Science Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
2Anusha V Illur, Department of Information Science Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
3Aekhata Nanda, Department of Information Science Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
4Eshwar Rao, Department of Information Science Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
5Vidyashree K P, Department of Information Science Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka India.
6Mansoor Ahmed, Mysuru Medical College, Mysuru, Karnataka, India.
Manuscript received on 05 April 2019 | Revised Manuscript received on 12 April 2019 | Manuscript Published on 26 July 2019 | PP: 245-250 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F10490486S419/19©BEIESP | DOI: 10.35940/ijitee.F1049.0486S419
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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: Machine learning has become one of the top most emerging technologies in this era of digital revolution. The machine learning algorithms are being used in various fields and applications such as image recognition, speech recognition, classification, prediction, medical diagnosis etc. In medical domain, machine learning techniques have been successfully implemented to improve the accuracy of medical diagnosis and also to improve the efficiency and quality of health care. In this paper, we have analyzed the existing health care practice system and have proposed how machine learning techniques can be used for differential diagnosis of Tuberculosis and Pneumonia which are often misdiagnosed due to similar symptoms at early stages.
Keywords: Machine Learning, Tuberculosis, Pneumonia, Differential diagnosis, ID3, Naïve Bayes, Random Forest.
Scope of the Article: Machine Learning