A Comprehensive Analysis Using Maximum Likelihood Estimation and Artificial Neural Networks for Modeling Arthritic Pain Relief Data

G S Deepthy (), Areekara Sujesh () and Sebastian Nicy ()
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G S Deepthy: Department of Statistics, St. Thomas College (Autonomous), Thrissur, affiliated to the University of Calicut, 680 001, Kerala, India
Areekara Sujesh: Department of Mathematics and Data Science, Sri. C. Achutha Menon Government College, Thrissur, 680 014, Kerala, India
Sebastian Nicy: Department of Statistics, St. Thomas College (Autonomous), Thrissur, affiliated to the University of Calicut, 680 001, Kerala, India

Stochastics and Quality Control, 2025, vol. 40, issue 1, 15-32

Abstract: The primary motivation behind this study is to precisely predicting the behaviour of the distribution by employing neural networks and enhancing its performance through maximum likelihood estimation. The numerical findings were compared to the predictions derived from the multilayer artificial neural network model developed with seven neurons in the hidden layer. The R value was 0.999 and the deviation values were less than 0.045 for the artificial neural network models. Also, the results of a numerical investigation using maximum likelihood estimation agree exactly with those obtained from predictions made using artificial neural networks. The findings of this study reveal that neural networks might be a very promising tool for clinical data analysis.

Keywords: Artificial Neural Network; Maximum Likelihood Estimation; Survival Function (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1515/eqc-2024-0023

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