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Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

Zeyad Ghaleb Al-Mekhlafi1, Ebrahim Mohammed Senan2, Taha H. Rassem3, Badiea Abdulkarem Mohammed4,5,*, Nasrin M. Makbol5, Adwan Alownie Alanazi1, Tariq S. Almurayziq1, Fuad A. Ghaleb6
1 Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il, Ha'il, 81481, Saudi Arabia
2 Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, 431004, India
3 Faculty of Science and Technology, Bournemouth University, Poole, BH12 5BB, UK
4 Department of Computer Engineering, College of Computer Science and Engineering, University of Ha'il, Ha'il, 81481, Saudi Arabia
5 College of Computer Science and Engineering, Hodeidah University, Hodeidah, 966, Yemen
6 School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor, 81310, Malaysia
* Corresponding Author: Badiea Abdulkarem Mohammed. Email:
(This article belongs to this Special Issue: Social Networks Analysis and Knowledge Management)

Computers, Materials & Continua 2022, 72(1), 775-796. https://doi.org/10.32604/cmc.2022.024492

Received 19 October 2021; Accepted 24 December 2021; Issue published 24 February 2022

Abstract

Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space. Meanwhile,the Recursive Feature Elimination algorithm (RFE) was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features. The features are fed into the various classification algorithms, namely, Support Vector Machine (SVM), K Nearest Neighbours (KNN), Decision Tree, Random Forest, and Multilayer Perceptron. All algorithms achieved superior results. The Random Forest algorithm achieved the best performance amongst the algorithms; it reached an overall accuracy of 99%. This algorithm classified stroke cases with Precision, Recall and F1 score of 98%, 100% and 99%, respectively. In the second dataset, the MRI image dataset was evaluated by using the AlexNet model and AlexNet + SVM hybrid technique. The hybrid model AlexNet + SVM performed is better than the AlexNet model; it reached accuracy, sensitivity, specificity and Area Under the Curve (AUC) of 99.9%, 100%, 99.80% and 99.86%, respectively.

Keywords

Stroke; cerebral haemorrhage; deep learning; machine learning; t-SNE and RFE algorithms

Cite This Article

Z. Ghaleb Al-Mekhlafi, E. Mohammed Senan, T. H. Rassem, B. Abdulkarem Mohammed, N. M. Makbol et al., "Deep learning and machine learning for early detection of stroke and haemorrhage," Computers, Materials & Continua, vol. 72, no.1, pp. 775–796, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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