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Machine intelligent diagnostic system (MIDs): an instance of medical diagnosis of tuberculosis

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Abstract

The article aims to review, analyze, design and implement a philosophy of medical diagnosis by artificial intelligence (AI) and soft computing techniques. The theme of the paper is that there are abundant corruptions in therapeutic judgment; in its place of appropriate diagnosis, majority practitioners go behind the narrow path by trapping the people at a serious phase. The ordinary community suffers deficient in diagnosis for higher investigative costs as well as the lack of certified practitioners. This article proposes some AI techniques to eradicate this lacuna by designing a prototype. The proposed prototype here termed as machine intelligent diagnostic system (MIDs), which has the capability of learning, thinking, reasoning and managing uncertainty as a real-world doctor. The model structured according to AI techniques considers the different cases of the disease to implement it. This article analyzes the shortcoming of MIDs, which can perform as a doctor to serve society as regular fashion as well as at the time of crisis as a crisis-manager. The degrees of the acuteness of patients’ symptoms are perceived by a membership function, which is used to tackle the emotion of the patients, and a fuzzy logic membership function is being used to calculate probabilities of diseases. Finally, this work finds smart results of MIDs, which can serve as doctors to some extent to compensate for the crisis of doctor in the universe.

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Abbreviations

AI:

Artificial intelligence

FL:

Fuzzy logic

SD:

Standard deviation

MF:

Membership function

M :

Mean

TB:

Tuberculosis

KB:

Knowledge base

LV:

Linguistic variable

5G:

Fifth generation

DFS:

Depth first search

References

  1. Das S, Biswas S, Paul A, Dey A (2018) AI doctor: an intelligent approach for medical diagnosis. In: Bhattacharyya S, Sen S, Dutta M, Biswas P, Chattopadhyay H (eds) Industry interactive innovations in science, engineering and technology, vol 11. Springer, Singapore, pp 173–183

    Chapter  Google Scholar 

  2. WHO | Unsafe drinking-water, sanitation and waste management, WHO, (2019). http://www.who.int/sustainable-development/cities/healthrisks/water-sanitation/en/. Accessed 1 Apr 2020

  3. Sekher TV (2012) Rural demography of India. In: Kulcsár LJ, Curtis KJ (eds) International handbook of rural demography, vol 3. Springer, Dordrecht, pp 169–189

    Chapter  Google Scholar 

  4. Tang A et al (2018) Canadian Association of Radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J 69(2):120–135. https://doi.org/10.1016/j.carj.2018.02.002

    Article  Google Scholar 

  5. Guo J, Li B (2018) The application of medical artificial intelligence technology in rural areas of developing countries. Health Equity 2(1):174–181. https://doi.org/10.1089/heq.2018.0037

    Article  Google Scholar 

  6. Shahid N, Rappon T, Berta W (2019) Applications of artificial neural networks in health care organizational decision-making: a scoping review. PLoS One. https://doi.org/10.1371/journal.pone.0212356

    Article  Google Scholar 

  7. Das S, Sanyal MK, Datta D (2019) Intelligent approaches for the diagnosis of low back pain. In: 2019 Amity international conference on artificial intelligence (AICAI), pp 684–695. Doi: 10.1109/AICAI.2019.8701266

  8. Sennaar K. Machine learning for medical diagnostics—4 current applications. Emerj. https://emerj.com/ai-sector-overviews/machine-learning-medical-diagnostics-4-current-applications/. Accessed 15 Jun 2019

  9. Alghamdi MA, Bhirud S, Alam MA (2014) Disease diagnosis using soft computing model: a digest. Int J Comput Appl. https://doi.org/10.5120/17855-8828

    Article  Google Scholar 

  10. Das S, Sanyal MK, Datta D (2020) Artificial Intelligent reliable doctor (AIRDr.): prospect of disease prediction using reliability. In: Mandal JK, Sinha D (eds) Intelligent computing paradigm: recent trends, vol 784. Springer, Singapore, pp 21–42

    Chapter  Google Scholar 

  11. Intelligent medical diagnostics—how artificial intelligence and machine learning improve disease detection rates. https://www.netguru.com/blog/intelligent-medical-diagnostics. Accessed 15 Jun 2019

  12. (6) INTERNIST-I, an experimental computer-based diagnostic consultant for general internal medicine. ResearchGate. https://www.researchgate.net/publication/16151132_INTERNIST-I_An_experimental_computer-based_diagnostic_consultant_for_general_internal_medicine. Accessed 15 Jun 2019

  13. Ogundele OA, Moodley D, Pillay A, Seebregts C (2016) An ontology for factors affecting tuberculosis treatment adherence behavior in sub-Saharan Africa. PPA. https://doi.org/10.2147/PPA.S96241

    Article  Google Scholar 

  14. Levine DM et al (2015) A tuberculosis ontology for host systems biology. Tuberculosis 95(5):570–574. https://doi.org/10.1016/j.tube.2015.05.012

    Article  Google Scholar 

  15. Andréka H, van Benthem J, Németi I (2017) On a new semantics for first-order predicate logic. J Philos Logic 46(3):259–267. https://doi.org/10.1007/s10992-017-9429-y

    Article  MathSciNet  MATH  Google Scholar 

  16. Das S, Sanyal M, Datta D, Biswas A (2018) AISLDr: Artificial intelligent self-learning doctor. In: Bhateja V, Coello Coello CA, Satapathy SC, Pattnaik PK (eds) Intelligent engineering informatics, vol 695. Springer, Singapore, pp 79–90

    Chapter  Google Scholar 

  17. Neurosurgery Education and Training School. https://www.aiimsnets.org/. Accessed 17 Jun 2019

  18. Rajasekaran S (2017) Tuberculosis of bones, joints, and spine: evidence-based management guide. Indian J Orthop 51(6):721. https://doi.org/10.4103/ortho.IJOrtho_502_17

    Article  Google Scholar 

  19. (10) Overview of extrapulmonary tuberculosis in adults and children|Request PDF. https://www.researchgate.net/publication/279947815_Overview_of_extrapulmonary_tuberculosis_in_adults_and_children. Accessed 17 Jun 2019

  20. Shende P, Valecha SM, Gandhewar M, Dhingra D (2017) Genital tuberculosis and infertility. Int J Reprod Contracept Obstet Gynecol 6(8):3514–3517. https://doi.org/10.18203/2320-1770.ijrcog20173474

    Article  Google Scholar 

  21. Harris HW, Menitove S (1994) Miliary tuberculosis. In: Schlossberg D (ed) Tuberculosis. Springer, New York, NY, pp 233–245

    Chapter  Google Scholar 

  22. Rathi P, Gambhire P (2016) Abdominal tuberculosis. J Assoc Physicians India 64(2):38–47

    Google Scholar 

  23. De Francesco Daher E, da Silva Junior GB, Barros EJG (2013) Renal tuberculosis in the modern era. Am J Trop Med Hyg 88(1):54–64. https://doi.org/10.4269/ajtmh.2013.12-0413

    Article  Google Scholar 

  24. Ocular Tuberculosis (TB)—Asia Pacific–American Academy of Ophthalmology. https://www.aao.org/topic-detail/ocular-tuberculosis-tb--asia-pacific-2. Accessed 17 Jun 2019

  25. Das A, Das SK, Pandit S, Basuthakur S (2015) Tonsillar tuberculosis: a forgotten clinical entity. J Fam Med Prim Care 4(1):124–126. https://doi.org/10.4103/2249-4863.152268

    Article  Google Scholar 

  26. Sonika U, Kar P (2012) Tuberculosis and liver disease: management issues. Trop Gastroenterol 33(2):102–106. https://doi.org/10.7869/tg.2012.25

    Article  Google Scholar 

  27. Hossain MS, Khalid MdS, Akter S, Dey S (2014) A belief rule-based expert system to diagnose influenza. In: 2014 9th international forum on strategic technology (IFOST), Cox’s Bazar, Bangladesh, pp 113–116. Doi: 10.1109/IFOST.2014.6991084

  28. Porcel JM (2009) Tuberculous pleural effusion. Lung 187(5):263–270. https://doi.org/10.1007/s00408-009-9165-3

    Article  Google Scholar 

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Acknowledgements

Thanks go to Dr(Prof.) M. K. Sanyal, University of Kalyani, who have advised and encouraged regularly for such kind of societal development and special thanks to Management, JIS College of Engineering, JIS GROUP, for providing all kinds of R&D resources.

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Authors and Affiliations

  1. Information Technology, JIS College of Engineering, Kalyani, 741235, India

    Sumit Das

  2. Department of Business Administration, University of Kalyani, Kalyani, 741235, India

    Manas Kumar Sanyal

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  1. Sumit Das
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  2. Manas Kumar Sanyal
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Contributions

Authors carried out the thought and realization of this work and explanation of outcome. Website data assist considerable assistance to the origin and blueprint of this work and decisively interpreting the diagnosis. The authors approved the paper.

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Correspondence to Sumit Das.

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Das, S., Sanyal, M.K. Machine intelligent diagnostic system (MIDs): an instance of medical diagnosis of tuberculosis. Neural Comput & Applic 32, 15585–15595 (2020). https://doi.org/10.1007/s00521-020-04894-8

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