Gingivitis Detection by Wavelet Energy Entropy and Linear Regression Classifier

Yan, Yan

doi:10.1007/978-3-030-84532-2_17

Cited by 1 publication

(1 citation statement)

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“…The same research also explains that the system design can be used as an initial treatment for the community and can be used as education for knowledge about this disease [13]. One of the implementations in handling these problems can be seen in a classification analysis model [14]. The classification analysis model can produce outputs by grouping disease on the weighted values obtained, that is severe symptoms of 0.693144314, moderate symptoms of 0.222716022, and symptoms of 0.084139664 [15].…”

Section: Introductionmentioning

confidence: 99%

Optimization artificial neural network classification analysis model diagnosis Gingivitis disease

Ariandi

Yanto²,

Jamhur³

et al. 2023

IJEECS

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<span lang="EN-US">Gingivitis is a disease that can be caused by the buildup of bacteria and plaque caused by leftover food. This disease can attack anyone, especially children who are not aware of maintaining dental and oral health. This study aims to build and optimize the classification analysis model for the diagnosis of Gingivitis. The classification analysis model was built using the artificial neural network (ANN) method which was optimized using fuzzy logic and the multiple linear regression (MRL) method. Optimization with fuzzy aims to develop a pattern of rules in the detection. The MRL method is also used as a process of measuring analysis patterns to ensure the analytical model presents maximum results. The results study indicate that the optimization of fuzzy and MRL methods provides excellent output. These results are based on the fuzzy output which can provide a pattern of 40 rules. The MRL method is can present the level of correlation of each analysis variable with a significant output having an average value of 94.2%. Based on the results of this study, the analysis model that is optimized with the fuzzy logic method and MRL contributes to maximizing the process of diagnosing Gingivitis.</span>

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Section: Introductionmentioning

confidence: 99%