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.
There are several meta-heuristic optimization algorithms developed on inspiration from nature. Artificial neural network proves to be efficient among other machine learning techniques. The efficiency of classification and prediction is improved by optimizing artificial neural network using the meta-heuristic optimization algorithms. The review of some of these hybrid artificial neural networks that are applied for benchmark datasets and to specific real-time experiments for classification and prediction are discussed. Upcoming sections cover the current trending research topics dealing with optimized artificial neural network concepts and provide some interesting insights for researchers to use in their respective applications domains of interest.
The early and accurate prediction of liver disease in patients is still a challenging task among medical practitioners even with latest advanced technologies. The support vector machines are widely used in medical domain. It has proved its efficiency on producing good diagnostic parameters. These results can be further improved by optimizing the hyperparameters of support vector machines. The proposed work is based on optimizing support vector machines with crow search algorithm. This optimized support vector machine classifier (CSA-SVM) is used for accurate diagnosis of Indian liver disease data. The various similar state of art algorithms are taken for comparison with proposed approach to prove its efficient. The performance of CSA-SVM is found to be outstanding among all other approaches in terms of all metrics taken for comparison. It has yielded the classification accuracy of 99.49%.
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