ArticleCureus2023
Modeling of Knowledge Toward Herbal Medicine for Oral Health Using Multiple Linear Regression and Neural Network.
Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
Background and goals Herbal medicine is used to treat a variety of oral health problems. Therefore, it is essential to comprehend it fully. To determine whether the amount used is risky, it is crucial to understand the dosages of medicinal plants. Before performing multiple linear regression (MLR) modeling, this paper uses the multilayer feedforward (MLFF) neural network (NN) technique to propose the variable selection. A data set with socio-demographic variables for dental staff and herbal medicine related to oral health knowledge score (KS) was chosen to demonstrate the design-build methodology. Materials and methods It was discovered that the KS is significantly related to the sex, age, income, occupation, and practice score (PS) at the first stage of the selection process, where all the variables were screened for their clinical importance. These five variables are chosen and used as inputs for the MLFF model by considering the level of significance, alpha = 0.05. Then, using the best variable discovered by the MLFF process, the MLR is applied. Results The performance of MLFF was evaluated using the mean squared error (MSE). MSE measures how far our estimates are off from the actual results. The MLFF's smallest MSE indicates the model's ideal combination of variable selection. Conclusion This study showed that using MLFF would help confirm the selected independent variables for MLR. In addition, KS level is more correlated with occupation, PS, and sex than with age and income. Moreover, this model could be used practically for any dataset. with the same criteria.
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