Classification of Imbalanced Fetal Health Data by PSO Based Ensemble Recursive Feature Elimination ANN

Gao, Jun; Huang, Canpeng; Huang, Kaishan; Wang, Hong

doi:10.1007/978-3-030-78811-7_29

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…To carry out the learning process, the ANN method can be used in classifying Otitis disease. ANN can solve problems effectively in the classification process [56]. In other cases, ANN is also a method that can allocate data for the classification process [57].…”

Section: Classification Analysis Processmentioning

confidence: 99%

Optimization deep learning with rough set approach model classification Otitis

Arief Wisky,

Ade Putra

2023

IJEECS

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<span>Otitis is a disease that occurs in the middle ear in the form of inflammation. This research aims to develop an analysis model for the classification of Otitis disease based on knowledge patterns based on symptoms and type of disease. The analysis methods used include the performance of the certainty factor (CF), rough set (RS), artificial neural network (ANN), and decision tree (DT) methods. CF and RS performance can be used to generate classification rule patterns. These rule patterns become new knowledge in the classification analysis process using the concept of deep learning (DL). DL analysis with ANN and DT performance can work optimally in exploring and discovering hidden knowledge. Based on the results of performance testing, the combination of CF and RS in preprocessing can present a classification pattern of 106 rules. The output of DL analysis results is proven to produce precise and accurate classification results with an accuracy of 89%. Based on these results, the analytical model developed was proven to be effective in classifying Otitis disease. Not only that, this research is also able to contribute to updating the knowledge-based system in the classification process.</span>

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Section: Classification Analysis Processmentioning

confidence: 99%