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A Fuzzy-Neuro-Based Clinical Decision Support System For Disease Diagnosis Using Symptom Severity

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Soft Computing and Signal Processing (ICSCSP 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1118))

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Abstract

Faster and accurate disease diagnosis is the need of the day. Various diagnostic tools are available to assist medical practitioners in the form of clinical decision support system (CDSS) and many more. This paper proposes to develop a CDSS that can assist medical practitioners with diagnostic decisions in general internal medicine for common diseases like malaria, typhoid, dengue which when ignored can cause epidemics. The proposed system aims at multi-disease diagnosis. Symptoms along with their severity are the input to the system. Most probable disease along with medication is the output of the system. The proposed system is modeled on neuro-fuzzy technique called adaptive neuro-fuzzy inference system (ANFIS) for disease diagnosis. Gaussian membership function is used as the fuzzifier, and custom defuzzifier is used to defuzzify the output. A rule-based system is used for medication and laboratory test recommendations. The proposed medical decision support system can aid medical practitioners in making better, effective, and faster diagnostic decisions, thereby helping in increasing the in-patient count and quality of medical care.

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References

  1. Miller, R.: Computer-assisted diagnostic decision support: history, challenges and possible paths forward. Adv. Health Sci. Educ. 14, 89–106 (2009)

    Article  Google Scholar 

  2. Uzoka, F.M.E., Osuji, J., Obot, O.: Clinical decision support system (DSS) in the diagnosis of malaria: a case comparison of two soft computing methodologies. Expert Syst. Appl. 38, 1537–1553 (2011)

    Google Scholar 

  3. Sangita, K., Deepa, G.: Association rule analysis in cardiovascular disease. In: 2016 Second International Conference on Cognitive Computing and Information Processing, pp. 1–6. IEEE Press, Mysore (2016)

    Google Scholar 

  4. Miller, R.A., McNeil, M.A., Challinor, S.M., Masarie, J.M., F.E, J.D.: The internist-1/quick medical reference project: status report. West J. Med. 145, 816–822 (1986)

    Google Scholar 

  5. Seixas, F.L., Bianca, Z., Jerson, L., Aura, C., Débora, C.M.S.: A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer’s disease and mild cognitive impairment. Comput. Biol. Med. 51, 140–158 (2014)

    Article  Google Scholar 

  6. Carvalho, C.M., Seixas, F.L., Conci, A., Muchaluat-Saade, D.C., Laks, J.: Improving a Bayesian decision model for supporting diagnosis of Alzheimer’s disease and related disorders. In: Perner, P. (eds.) Machine Learning and Data Mining in Pattern Recognition. MLDM 2017. Lecture Notes in Computer Science, vol. 10358. Springer, Cham (2017)

    Google Scholar 

  7. Tian, Y., Shang, Y., Tong, D.Y., Chi, S.-Q., Li, J., Kong, X.-X., Ding, K.-F., Li, J.-S.: POPCORN: a web service for individual PrognOsis prediction based on multi-center clinical data CollabORatioN without patient-level data sharing. J. Biomed. Inf. 86, 1–14 (2018)

    Google Scholar 

  8. Shen, Y., Yuan, K., Chen, D., Colloc, J., Yang, M., Li, Y., Lei, K..: An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription. Artif. Intell. Med. 86, 20–32 (2018)

    Google Scholar 

  9. Bauer, S., Köhler, S., Marcel, H.S., Peter, N.R.: Bayesian ontology querying for accurate and noise-tolerant semantic searches. Bioinformatics 28, 2502–2508 (2018)

    Article  Google Scholar 

  10. Samuel, O.W., Omisore, M.O., Ojokoh, B.A.: A web based decision support system driven by fuzzy logic for the diagnosis of typhoid fever. Expert Syst. Appl. 40, 4164–4171 (2013)

    Article  Google Scholar 

  11. Saikia, D., Jiten, C.D.: Early diagnosis of dengue disease using fuzzy inference system. In: 2016 International Conference on Microelectronics, Computing and Communications (MicroCom), pp. 1–6. IEEE Press, Durgapur (2016)

    Google Scholar 

  12. Malmir, B., Mohammadhossein, A., Shing, I.C.: A medical decision support system for disease diagnosis under uncertainty. Expert Syst. Appl. 88, 95–108 (2017)

    Article  Google Scholar 

  13. Kadhim, M.A.: FNDSB: A fuzzy-neuro decision support system for back pain diagnosis. Cogn. Syst. Res. 52, 691–700 (2018)

    Article  Google Scholar 

  14. Madhu, D., Jain, C.N., Sebastain, E., Shaji, S., Ajayakumar, A.: A novel approach for medical assistance using trained chatbot. In: International Conference on Inventive Communication and Computational Technologies, pp. 243–246. IEEE Press, Coimbatore (2017)

    Google Scholar 

  15. Chung, K., Roy, C.P.: Chatbot-based healthcare service with a knowledge base for cloud computing. Cluster Comput. (2018)

    Google Scholar 

  16. Vaira, L., Bochicchio, M.A., Conte, M., Casaluci, F.M., Melpignano, A.: MamaBot: a system based on ML and NLP for supporting Women and families during pregnancy. In: Proceedings of 22nd International Database Engineering & Applications Symposium (2018)

    Google Scholar 

  17. Vinitha, D., Deepa,G., Sangita, K., Ashish, A.: Investigation of chronic disease correlation using data mining techniques. In: 2nd International Conference on Recent Advances in Engineering & Computational Sciences, Chandigarh (2015)

    Google Scholar 

  18. Shastri, S.S., Nair, P.C., Gupta, D., Nayar R.C., Rao, R., Ram, A.: Breast Cancer diagnosis and prognosis using machine learning techniques. In: Thampi, S., Mitra, S., Mukhopadhyay, J., Li, K.C., James, A., Berretti, S. (eds.) Intelligent Systems Technologies and Applications, ISTA 2017. Advances in Intelligent Systems and Computing, vol 683. (2018)

    Google Scholar 

  19. Yaseen, Z.M., Ghareb, M.I., Ebtehaj, I., Bonakdari, H., Siddique, R., Heddam, S., Yusif, A.A., Deo, R.: Rainfall pattern forecasting using novel hybrid intelligent model based ANFIS-FFA. Water Resources Management, vol. 32 (2018)

    Google Scholar 

  20. Hasanipanah, M., Amnieh, H.B., Arab, H., Zamzam, S.B.: Feasibility of PSO-ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput. Appl., vol. 30 (2018)

    Google Scholar 

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Acknowledgement

We acknowledge the feedback given by Dr. Keshav, Dr. Shailaja, Dr. Vishnu, Dr. Rama Raj, and Dr. Priyamvada on the symptom severity of the eight diseases that are considered in our study. Medical experts were consulted to provide insight for quantifying symptom severity. Based on the medical expert feedback, synthetic data were created which was used for feasibility study of our proposed model. Clinical patient data were never used for the creation of synthetic data in our study.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India

    Sulochana Tandra, Deepa Gupta & J. Amudha

  2. Paralaxiom Technologies Private Limited, Bengaluru, India

    Kshitij Sharma

Authors
  1. Sulochana Tandra
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  2. Deepa Gupta
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  3. J. Amudha
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  4. Kshitij Sharma
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Editor information

Editors and Affiliations

  1. Department of ECE, Malla Reddy College of Engineering and Technology, Hyderabad, Telangana, India

    V. Sivakumar Reddy

  2. Department of CSE, Jawaharlal Nehru Technological University Hyderabad, Hyderabad, Telangana, India

    V. Kamakshi Prasad

  3. Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ, USA

    Jiacun Wang

  4. Department of ECE, Sir Visvesvaraya Institute of Technology, Nashik, Maharashtra, India

    K. T. V. Reddy

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Tandra, S., Gupta, D., Amudha, J., Sharma, K. (2020). A Fuzzy-Neuro-Based Clinical Decision Support System For Disease Diagnosis Using Symptom Severity. In: Reddy, V., Prasad, V., Wang, J., Reddy, K. (eds) Soft Computing and Signal Processing. ICSCSP 2019. Advances in Intelligent Systems and Computing, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-15-2475-2_9

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