Classification of osteoporosis by artificial neural network based on monarch butterfly optimisation algorithm
| Autor: | R. Joshua Samuel Raj, D. Devikanniga |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
clinical methods
Wilcoxon signed-rank test osteoporosis diagnosis optimisation Osteoporosis menopause monarch butterfly optimisation-based artificial neural network classifier 02 engineering and technology medical diagnostic computing bone hybrid classifier model 0302 clinical medicine Health Information Management sensitivity analysis 0202 electrical engineering electronic engineering information engineering 10-fold cross-validation method life threatening disease Bone mineral Artificial neural network monarch butterfly optimisation algorithm osteoporotic patient neural nets medicine.anatomical_structure lcsh:R855-855.5 osteoporosis classification 020201 artificial intelligence & image processing Radiology medicine.medical_specialty lcsh:Medical technology lumbar spine dataset Health Informatics Article diseases mild bone fractures 03 medical and health sciences pattern classification medicine femoral neck dataset Femoral neck receiver operating characteristics analysis Receiver operating characteristic business.industry skeletal regions 030206 dentistry medicine.disease BMD values Orthopedic surgery patient diagnosis business bone mineral density Classifier (UML) orthopaedics |
| Zdroj: | Healthcare Technology Letters Healthcare Technology Letters (2018) |
| ISSN: | 2053-3713 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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