Diagnosis of osteoporosis using intelligence of optimized extreme learning machine with improved artificial algae algorithm
Osteoporosis is the silent killer disease that mostly occur in elderly people because of bone fragility and fracture. Early and accurate diagnosis of osteoporosis saves the patient life. This work focuses on developing an efficient classifier model to support this issue. For this, the proven Extreme...
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| Veröffentlicht in: | International journal of intelligent networks Jg. 1; S. 43 - 51 |
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2020
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| Abstract | Osteoporosis is the silent killer disease that mostly occur in elderly people because of bone fragility and fracture. Early and accurate diagnosis of osteoporosis saves the patient life. This work focuses on developing an efficient classifier model to support this issue. For this, the proven Extreme learning machine (ELM) is selected. Then a novel hybrid metaheuristic optimization algorithm is developed on fusing two nature inspired metaheuristic optimization algorithms namely Artificial algae algorithm with multi-light source and Monarch butterfly optimization algorithm. It is named as HMBA algorithm. To further increase the diagnostic accuracy of ELM, it is optimized using HMBA. This proposed HMBA-ELM classifier model is used to diagnose osteoporosis from normal subjects. The discrimination efficiency of proposed classifier is compared with other similar classifiers based on the results produced. It is found that the proposed HMBA- ELM has yielded outstanding results mainly in terms of (sensitivity ± SD/specificity ± SD/precision ± SD/MCR±SD/accuracy ±SD) as (99.45 ± 0.69/99.77 ± 0.31/96.32 ± 0.12/0.30 ± 0.18/99.70 ± 0.21), (98.11 ± 0.91/99.56 ± 0.28/90.03 ± 0.19/0.51 ± 0.09/99.49 ± 0.18) and (99.26 ± 1.13/99.54 ± 0.33/97.38 ± 0.22/0.4 ± 0.31/99.6 ± 0.32) respectively for three osteoporosis datasets namely Femoral neck, Lumbar spine and Femoral & Spine. This is highest among all other approaches with less computation time.
•The novel HMBA algorithm is developed by fusing AAAML and MBO algorithm.•This procedure is named as Evolution – migrated AAAML.•The HMBA algorithm is used to train ELM for classification of osteoporosis datasets used in the study.•The experiments are conducted and evaluated using 10- fold cross validation on ten independent runs.•HMBA-ELM has produced higher accuracy of 99.7% in classifying osteoporotic datasets. |
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| AbstractList | Osteoporosis is the silent killer disease that mostly occur in elderly people because of bone fragility and fracture. Early and accurate diagnosis of osteoporosis saves the patient life. This work focuses on developing an efficient classifier model to support this issue. For this, the proven Extreme learning machine (ELM) is selected. Then a novel hybrid metaheuristic optimization algorithm is developed on fusing two nature inspired metaheuristic optimization algorithms namely Artificial algae algorithm with multi-light source and Monarch butterfly optimization algorithm. It is named as HMBA algorithm. To further increase the diagnostic accuracy of ELM, it is optimized using HMBA. This proposed HMBA-ELM classifier model is used to diagnose osteoporosis from normal subjects. The discrimination efficiency of proposed classifier is compared with other similar classifiers based on the results produced. It is found that the proposed HMBA- ELM has yielded outstanding results mainly in terms of (sensitivity ± SD/specificity ± SD/precision ± SD/MCR±SD/accuracy ±SD) as (99.45 ± 0.69/99.77 ± 0.31/96.32 ± 0.12/0.30 ± 0.18/99.70 ± 0.21), (98.11 ± 0.91/99.56 ± 0.28/90.03 ± 0.19/0.51 ± 0.09/99.49 ± 0.18) and (99.26 ± 1.13/99.54 ± 0.33/97.38 ± 0.22/0.4 ± 0.31/99.6 ± 0.32) respectively for three osteoporosis datasets namely Femoral neck, Lumbar spine and Femoral & Spine. This is highest among all other approaches with less computation time.
•The novel HMBA algorithm is developed by fusing AAAML and MBO algorithm.•This procedure is named as Evolution – migrated AAAML.•The HMBA algorithm is used to train ELM for classification of osteoporosis datasets used in the study.•The experiments are conducted and evaluated using 10- fold cross validation on ten independent runs.•HMBA-ELM has produced higher accuracy of 99.7% in classifying osteoporotic datasets. |
| Author | Devikanniga, D. |
| Author_xml | – sequence: 1 givenname: D. surname: Devikanniga fullname: Devikanniga, D. email: mail4kanniga@gmail.com organization: Department of Computer Science and Engineering, Anna University, Chennai, 600025, India |
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| Cites_doi | 10.1093/qjmed/hcn022 10.1007/s00521-015-1923-y 10.1016/j.knosys.2014.04.042 10.1007/s11063-012-9236-y 10.1007/s12018-009-9064-4 10.1007/s001980050093 10.35940/ijitee.C8378.019320 10.1109/ETFA.2013.6647975 10.1001/jama.285.6.785 10.1002/jbmr.5650090621 10.1016/j.biosystems.2015.11.004 10.1359/jbmr.1997.12.11.1761 10.1016/j.jocd.2007.12.007 10.1142/S0218213014500146 10.1016/j.neucom.2016.06.023 10.1111/j.1467-8667.1994.tb00369.x 10.1016/j.patcog.2005.03.028 10.1162/neco.1991.3.4.617 10.1049/htl.2017.0059 10.1007/s00198-014-2794-2 10.1016/j.swevo.2015.05.003 |
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| Keywords | Metaheuristic optimization Osteoporosis Monarch butterfly optimization Artificial algae algorithm Classification |
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| Snippet | Osteoporosis is the silent killer disease that mostly occur in elderly people because of bone fragility and fracture. Early and accurate diagnosis of... |
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| SubjectTerms | Artificial algae algorithm Classification Metaheuristic optimization Monarch butterfly optimization Osteoporosis |
| Title | Diagnosis of osteoporosis using intelligence of optimized extreme learning machine with improved artificial algae algorithm |
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