Classification of osteoporosis by artificial neural network based on monarch butterfly optimisation algorithm. Issue 2 (16th February 2018)

• Record Type: Journal Article
• Authors: Devikanniga, D.; Joshua Samuel Raj, R.
• Is Part Of: Healthcare technology letters. Volume 5:Issue 2(2018)
• Page Start: 70
• Page End: 75
• Publication Date: 2018-02-16
• Subjects: diseases -- bone -- neural nets -- optimisation -- sensitivity analysis -- pattern classification -- medical diagnostic computing -- patient diagnosis -- orthopaedics; Wilcoxon signed‐rank test -- receiver operating characteristics analysis -- lumbar spine dataset -- femoral neck dataset -- 10‐fold cross‐validation method -- monarch butterfly optimisation‐based artificial neural network classifier -- osteoporotic patient -- hybrid classifier model -- skeletal regions -- clinical methods -- BMD values -- bone mineral density -- osteoporosis diagnosis -- mild bone fractures -- menopause -- life threatening disease -- monarch butterfly optimisation algorithm -- osteoporosis classification; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1049/htl.2017.0059
• Languages: English
• ISSNs: 2053-3713

Abstract

Abstract : Osteoporosis is a life threatening disease which commonly affects women mostly after their menopause. It primarily causes mild bone fractures, which on advanced stage leads to the death of an individual. The diagnosis of osteoporosis is done based on bone mineral density (BMD) values obtained through various clinical methods experimented from various skeletal regions. The main objective of the authors' work is to develop a hybrid classifier model that discriminates the osteoporotic patient from healthy person, based on BMD values. In this Letter, the authors propose the monarch butterfly optimisation‐based artificial neural network classifier which helps in earlier diagnosis and prevention of osteoporosis. The experiments were conducted using 10‐fold cross‐validation method for two datasets lumbar spine and femoral neck. The results were compared with other similar hybrid approaches. The proposed method resulted with the accuracy, specificity and sensitivity of 97.9% ± 0.14, 98.33% ± 0.03 and 95.24% ± 0.08, respectively, for lumbar spine dataset and 99.3% ± 0.16%, 99.2% ± 0.13 and 100, respectively, for femoral neck dataset. Further, its performance is compared using receiver operating characteristics analysis and Wilcoxon signed‐rank test. The results proved that the proposed classifier is efficient and it outperformed the other approaches in all the cases.