Abstract
The massive growth of data volume within the healthcare sector pushes the current classical systems that were adapted to the limit. Recent studies have focused on the use of machine learning methods to develop healthcare systems to extract knowledge from data by means of analysing, mining, pattern recognition, classification and prediction. Our research study reviews and examines different supervised machine learning classifiers using headache dataset. Different statistical measures have been used to evaluate the performance of seven well-known classifiers. The experimental study indicated that Decision Tree classifier achieved a better overall performance, followed by Artificial Neural Network, Support Vector Machine and k-Nearest Neighbor. This would determine the most suitable classifier for developing a particular classification system that is capable of identifying primary headache disorders.
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Aljaaf, A.J., Al-Jumeily, D., Hussain, A.J., Fergus, P., Al-Jumaily, M., Radi, N. (2015). A Systematic Comparison and Evaluation of Supervised Machine Learning Classifiers Using Headache Dataset. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_12
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DOI: https://doi.org/10.1007/978-3-319-22053-6_12
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