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Early Stage Lung Cancer Prediction Using Various Machine Learning Techniques

Publication Type : Conference Proceedings

Publisher : Advances in Intelligent Systems and Computing

Source : Advances in Intelligent Systems and Computing, Springer, Volume 1118, p.81-98 (2020)

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Keywords : ANFIS, CDSS, Fuzzy-neuro, K-means

Campus : Bengaluru

School : School of Engineering

Department : Computer Science

Year : 2020

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.

Cite this Research Publication : S. Tandra, Gupta, D., Amudha J., and Sharma, K., “A Fuzzy-Neuro-Based Clinical Decision Support System For Disease Diagnosis Using Symptom Severity”, Advances in Intelligent Systems and Computing, vol. 1118. Springer, pp. 81-98, 2020.

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