Handling Imbalanced Class Problem of Measles Infection Risk Prediction Model
Wan Muhamad Taufik Wan Ahmad1, NurLaila Ab Ghani2, Sulfeeza Mohd Drus3
1Wan MuhamadTaufik Wan Ahmad*, College of Computing and Informatics, University Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang, Selangor, Malaysia.
2NurLaila Ab Ghani, College of Computing and Informatics, University Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang, Selangor, Malaysia.
3Sulfeeza Mohd Drus, College of Computing and Informatics, University Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang, Selangor, Malaysia.
Manuscript received on September 01, 2019. | Revised Manuscript received on September 22, 2019. | Manuscript published on October 30, 2019. | PP: 3431-3435 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2649109119/2019©BEIESP | DOI: 10.35940/ijeat.A2649.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study explores a novel application of multi scale bubble entropy analysis with power metric analysis to achieve efficient epileptic seizure prediction performance. Method-This paper aims to develop a reliable seizure detection technique that incorporates AM FM model for decomposition of EEG into different sub bands. The initially first feature set is formed by acquiring the absolute and relative power components at each electrode. Second feature set is constructed by multi scale bubble entropy analysis from each sub band. These two major feature vectors are fuse into an integrated feature space to perform classification task using ANN.
Keywords: Data Mining, Classification, Measles, Imbalanced Data