Fuzzy expert system for diagnosis of Breast Cancer

Ohri, Kalyani; Singh, Harjit; Sharma, Anurag

doi:10.1109/wispnet.2016.7566591

Cited by 8 publications

(1 citation statement)

References 3 publications

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“…Gulbandilar et al [13] constructed a fuzzy logic algorithm to identify low back pain intensity by using data of 169 patients. A fuzzy expert system was developed by Ohri et al [14] to diagnose breast cancer. Gal et al [15] proposed a fuzzy expert system to predict subchondral sclerosis.…”

Section: Introductionmentioning

confidence: 99%

Type-2 Fuzzy Rule-Based Expert System for Diagnosis of Spinal Cord Disorders

et al. 2018

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The majority of people have experienced pain in their low back or neck in their lives. In this paper, a type-2 fuzzy rule-based expert system is presented for diagnosing the spinal cord disorders. The interval type-2 fuzzy logic system permits us to handle the high uncertainty of diagnosing the type of disorder and its severity. The spinal cord disorders are studied in ve categories using historical data and clinical symptoms of the patients. The main novelty of this paper lies in presentation of the interval type-2 fuzzy hybrid rule-based system, which is a combination of the forward and backward chaining approaches in its inference engine and avoids unnecessary medical questions. Use of parametric operations for fuzzy calculations increases the robustness of the system and the compatibility of the diagnosis with a wide range of physicians' diagnosis. The outputs of the system are comprised of type of disorder, location, and severity as well as the necessity of taking an M.R. Image. A comparison of the performance of the developed system with the expert shows an acceptable accuracy of the system in diagnosing the disorders and determining the necessity of the M.R. Image.

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Section: Introductionmentioning

confidence: 99%