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Classification of osteoporosis by artificial neural network based on monarch butterfly optimisation algorithm

机译：基于帝王蝶优化算法的人工神经网络对骨质疏松症的分类

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摘要

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.
机译：骨质疏松症是一种危及生命的疾病，通常会在绝经后影响女性。它主要导致轻度骨折，晚期则导致个人死亡。骨质疏松症的诊断是基于通过从各种骨骼区域进行实验的各种临床方法获得的骨矿物质密度（BMD）值进行的。作者工作的主要目的是开发一种混合分类器模型，该模型基于BMD值将骨质疏松症患者与健康人区分开。在这封信中，作者提出了基于帝王蝶优化的人工神经网络分类器，该分类器有助于早期诊断和预防骨质疏松症。使用10倍交叉验证方法对腰椎和股骨颈的两个数据集进行了实验。将结果与其他类似的混合方法进行了比较。该方法对腰椎数据集的准确性，特异性和灵敏度分别为97.9％±0.14、98.33％±0.03和95.24％±0.08，对于腰椎数据集分别为99.3％±0.16％，99.2％±0.13和100。股骨颈数据集。此外，使用接收机工作特性分析和Wilcoxon符号秩检验来比较其性能。结果证明，所提出的分类器是有效的，并且在所有情况下均优于其他方法。

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