An Expert System to Diagnose Spinal Disorders

Seyed M.S. Dashti1, *, Seyedeh F. Dashti2
1 Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran
2 Department of Advanced Research, Bushehr University of Medical Sciences, Bushehr, Iran

Article Metrics

CrossRef Citations:
Total Statistics:

Full-Text HTML Views: 312
Abstract HTML Views: 197
PDF Downloads: 103
Total Views/Downloads: 612
Unique Statistics:

Full-Text HTML Views: 199
Abstract HTML Views: 112
PDF Downloads: 79
Total Views/Downloads: 390

Creative Commons License
© 2020 Dashti and Dashti.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: ( This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran, E-mail:



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.


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.


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.


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.

Keywords: Spine, Expert system, Uncertainty factor, Spinal disorder, Knowledge engineering, Knowledge representation.