Medical expert system for low back pain management: design issues and conflict resolution with Bayesian network
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ORIGINAL ARTICLE
Medical expert system for low back pain management: design issues and conflict resolution with Bayesian network Debarpita Santra 1
&
Jyotsna Kumar Mandal 1 & Swapan Kumar Basu 2 & Subrata Goswami 3
Received: 8 July 2019 / Accepted: 25 June 2020 # International Federation for Medical and Biological Engineering 2020
Abstract The paper focuses on the development of a reliable medical expert system for diagnosis of low back pain (LBP) by proposing an efficient frame-based knowledge representation scheme and a suitable resolution logic with conflicts in outcomes being resolved using Bayesian network. Considering that LBP is classified into many diseases based on different pain generators, the proposed methodology infers non-conflicting LBP diseases sorted according to their chances of occurrence. A satisfactory clinical efficacy (average relative error − 0.09, recall 74.44%, precision 76.67%, accuracy 71.11%, and F1-score 73.88%) of the proposed methodology has been found after validating the design with empirically selected thirty LBP patient cases. Constraining that an inferred disease having chance of occurrence, prior to pathological investigations, below 0.75 (as set by four pain specialists) is not accepted clinically; the design can correctly identify, on average, 74.44% of actual diagnosis; and 76.67% of inferred diagnosis is included in actual diagnosis. With the predicted chance of occurrence being lower than 0.75 by a fraction of 0.09 on average, the proposed design performs well for 73.88% cases detecting 71.11% inferred outcomes as accurate. The design offers homogeneity to the actual outcomes, with the chi-squared static being calculated as 11.08 having 12 as degree of freedom. Keywords Medical expertsystem . Low backpain management . Knowledgerepresentation . Inference engine . Bayesiannetwork
1 Introduction LBP [1] is the leading cause of activity limitation and work absence throughout much of the world with enormous economic burden [2], having an estimated prevalence of 70 to 85% [3]. With the reason behind LBP being unclear except the specific causes (fracture, neoplasm, and infection) for approximately 5–10% cases [4], LBP is generally said to be nonspecific. Based on a number of pain generators (muscles, joints, intervertebral discs, nerve roots etc.) [5], non-specific LBP may be classified into different disorders or diseases
* Debarpita Santra [email protected] 1
Department of Computer Science and Engineering, Faculty of Engineering, Technology and Management, University of Kalyani, Block C, Nadia, Kalyani, West Bengal 741245, India
2
Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India
3
ESI Institute of Pain Management,, ESI Hospital Sealdah premises, 301/3 Acharya Prafulla Chandra Road, Kolkata, West Bengal 700009, India
namely sacroiliac joint arthropathy or SIJA (pain from sacroiliac joint) [6], facet joint arthropathy or FJA (pain from lumbar facet joints) [7], Discogenic Pain or DP (pain from
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