Usage and implementation of neuro-fuzzy systems for classification and prediction in the diagnosis of different types of
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Usage and implementation of neuro‑fuzzy systems for classification and prediction in the diagnosis of different types of medical disorders: a decade review Haneet Kour1 · Jatinder Manhas2 · Vinod Sharma1
© Springer Nature B.V. 2020
Abstract In classification and prediction of different types of medical disorders the neuro-fuzzy systems (NFS) are playing vital and significant role. To avoid false diagnosis the NFS assists medical practitioners to a greater extent in automating the domain dealing with medical disorders. With the passage of time the NFS approach has become apparent to enhance accuracy in dealing with a wide range of complicated research problems in the field of medical diagnosis. In this paper the author presents the literature review of the research done in implementing NFS in the field of medical diagnosis for current decade. Total of 100 publications in chronological advancement and up-gradation in models are considered for the time period of 10 years. A detailed study of each disease is carried out to discuss how NFS methodologies have been applied for classification and prediction in the diagnosis of different types of medical disorders. Ten (10) most severe medical disorders i.e. cancer, cardiovascular, depression and anxiety, diabetes, communicable, kidney, liver, neuro-degenerative, respiratory and thyroid has been undertaken for the study. Based on the study carried out it has been observed that NFS found to be effective as compared to the application of other AI techniques in medical diagnosis. Study reveals that effectiveness of NFS increases significantly when integrated with other AI approaches. This review adds into the knowledge of different researchers working in the field of medical diagnosis and will also give the comprehensive view of the effectiveness of the NFS techniques being used in medical diagnosis. The paper also incorporates a few research publications that were submitted in 2019 to incorporate the latest advances in medical science implementation of NFS. Keywords Neural network · Medical diagnosis · Fuzzy inference engine · NFS
* Haneet Kour [email protected] Jatinder Manhas [email protected] Vinod Sharma [email protected] 1
Department of Computer Science and IT, University of Jammu, Jammu, India
2
Department of Computer Science and IT, Bhaderwah Campus, University of Jammu, Jammu, India
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H. Kour et al.
1 Introduction Medical diagnosis relates to the procedure tailored to define the reason for the sickness or discomfort of the patient, i.e. to determine the disease using the historical facts and symptoms of the patient. The diagnostic process usually starts with simple physical examination of the person seeking medical care and then accordingly diagnostic tests (such as blood tests, magnetic resonance imaging (MRI) scan, ultrasound scan, PET scan, electrocardiogram (ECG) and so on) are performed for the confirmation of the patient’s specific illness (https://en.wikipedia.org/wiki/Medical_diagnosis). Medical decision
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