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

  • Sulochana Tandra
  • Deepa Gupta
  • J. Amudha
  • Kshitij Sharma
Conference paper
  • 19 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1118)

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.

Keywords

ANFIS CDSS Fuzzy-neuro K-means 

Notes

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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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Sulochana Tandra
    • 1
  • Deepa Gupta
    • 1
  • J. Amudha
    • 1
  • Kshitij Sharma
    • 2
  1. 1.Department of Computer Science and EngineeringAmrita School of Engineering, Amrita Vishwa VidyapeethamBengaluruIndia
  2. 2.Paralaxiom Technologies Private LimitedBengaluruIndia

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