What question did this study set out to answer?

The research aims to develop a radial basis artificial neural network to model diarrhea disease, accounting for treatment and vaccination effects.

March 28, 2026

A radial basis neural network for the diarrhea disease model including treatment and vaccination

Key Points

The research aims to develop a radial basis artificial neural network to model diarrhea disease, accounting for treatment and vaccination effects.
Developed a stochastic radial basis artificial neural network (RB-ANN) using scale conjugate gradient (SCG) optimization.
Employed a susceptible-infected-recovered model to incorporate treatment and vaccination.
Utilized a dataset optimized through the Adam solver with a focus on minimizing mean square error.
Implemented a radial basis function with sixteen neurons in hidden layers.
Evaluated model performance through comparison of predicted and reference results.
Achieved a mean square error with 75% training, 13% authentication, and 12% testing data distributions.
Demonstrated high accuracy with an insignificant absolute error.
Validated model performance using histogram curves, function fitness, and correlation/regression analysis.

Abstract

The aim of this study is to propose a stochastic radial basis (RB) artificial neural network (ANN) and the scale conjugate gradient (SCG) called as RB-ANN-SCG for the the diarrhea disease model including treatment and vaccination (DDMTV). The diarrhea disease system is basically a susceptible, infected and recovered model that includes the factor of treatment and vaccination. The dataset is obtained by using the Adam solver, which lessens the mean square error with the distribution of testing (12%), authentication (13%), and training (75%). A transfer radial basis function together with sixteen neurons is used in the hidden layers for solving the DDMTV, while the optimization is performed by the SCG. The scheme’s correctness is obtained via overlapping of the reference and achieved results. The insignificant calculated absolute error and best validation performances present the accuracy of the solver. Furthermore, the constancy and dependability of the RB-ANN-SCG is pragmatic through the histogram curves, function fitness, and correlation/regression for solving the DDMTV.

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