Abstract
Integration of Internet of Healthcare Things (IoHT) and Machine Learning (ML) can be used successfully in healthcare systems to increase the accuracy of computer-aided diagnosis. This paper emphases on the application of IoHT and ML in detecting spinal abnormalities, which can be integrated with IoHT. The novelty of this work is in the use of multilayer perceptron (MLP) to a spinal dataset to obtain high accuracy in spinal abnormality detection. The dataset has 310 samples and is freely available on Kaggle repository. We use Pearson correlation coefficient (PCC), ReliefF and Gain ratio (GR) filter-based feature selection methods to select the top 10, 8, 6 and 5 features according to relevance or weight of features in preprocessing stage. In classification stage, we use logistic regression (LR), support vector machine (SVM), and Bagging algorithm in addition to MLP. The experimental results indicate that the PCC feature selection technique and MLP classification algorithms give the most promising results. A maximum classification accuracy of 88.0645% is obtained when MLP is used after selecting the top 5 spinal features by PCC feature selection method. This accuracy obtained by MLP and PCC is higher than 86.96% reported in the literature of spinal disease.
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Begum, A.M., Mondal, M.R.H., Podder, P., Bharati, S. (2022). Detecting Spinal Abnormalities Using Multilayer Perceptron Algorithm. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_62
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