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

Author

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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)

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.

Suggested Citation

  • G S Deepthy & Areekara Sujesh & Sebastian Nicy, 2025. "A Comprehensive Analysis Using Maximum Likelihood Estimation and Artificial Neural Networks for Modeling Arthritic Pain Relief Data," Stochastics and Quality Control, De Gruyter, vol. 40(1), pages 15-32.
    • Handle: RePEc:bpj:ecqcon:v:40:y:2025:i:1:p:15-32:n:1002
    DOI: 10.1515/eqc-2024-0023
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