Design of a Medical Expert System (MES) Based on Rough Set Theory for Detection of Cardiovascular Diseases

Chaudhuri, Sangeeta Bhanja; Rahman, Mirzanur

doi:10.1007/978-981-10-6872-0_30

Cited by 2 publications

(2 citation statements)

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“…In this method, markers were installed on the naked body, and images of the individual's movement were recorded through the motion analyzer camera. Comparing their method with a flexible ruler, the researchers observed results that showed a Pearson correlation coefficient of 97% for kyphosis and 95% for lumbar lordosis [2]. This method involves some disadvantages, as it:…”

Section: Literature Reviewmentioning

confidence: 99%

“…In these systems, experts' knowledge in a particular field of science is transferred to a computer, as a result of which experts' way of thinking in that particular area can be emulated. More speci-fically, the ES detects the logical models through which an expert makes a decision, making judgments similar to those of human beings [1,2]. One of the specialized topics in physiotherapy and physical education is the detection of abnormalities.…”

Section: Introductionmentioning

confidence: 99%

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An Expert System to Diagnose Spinal Disorders

Dashti¹,

Dashti²

2020

TOBIOIJ

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Objective: Until now, traditional invasive approaches have been the only means being leveraged to diagnose spinal disorders. Traditional manual diagnostics require a high workload, and diagnostic errors are likely to occur due to the prolonged work of physicians. In this research, we develop an expert system based on a hybrid inference algorithm and comprehensive integrated knowledge for assisting the experts in the fast and high-quality diagnosis of spinal disorders. Methods: First, for each spinal anomaly, the accurate and integrated knowledge was acquired from related experts and resources. Second, based on probability distributions and dependencies between symptoms of each anomaly, a unique numerical value known as certainty effect value was assigned to each symptom. Third, a new hybrid inference algorithm was designed to obtain excellent performance, which was an incorporation of the Backward Chaining Inference and Theory of Uncertainty. Results: The proposed expert system was evaluated in two different phases, real-world samples, and medical records evaluation. Evaluations show that in terms of real-world samples analysis, the system achieved excellent accuracy. Application of the system on the sample with anomalies revealed the degree of severity of disorders and the risk of development of abnormalities in unhealthy and healthy patients. In the case of medical records analysis, our expert system proved to have promising performance, which was very close to those of experts. Conclusion: Evaluations suggest that the proposed expert system provides promising performance, helping specialists to validate the accuracy and integrity of their diagnosis. It can also serve as an intelligent educational software for medical students to gain familiarity with spinal disorder diagnosis process, and related symptoms.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%