aspg-31-2987-1791686659 20261011024419 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Advances in Applied Computational Intelligence IJAACI 2833-5600 2024 5 2 Basheer Abd Al Rida Sadiq Al-Imam Al-Kadhum College for Islamic Science University, Iraq Murhaf Obaidi Mustansiriah University, Department of Mathematics, Iraq With the large-scale language methods namely ChatGPT, there is a chance to explore the use of machine learning (ML) methods on ChatGPT-generated data for classifying healthcare data. Healthcare data classification gains more significance in extracting and organizing useful insights from the huge volume of medical data available. The ChatGPT-generated data has realistic and different healthcare-based text datasets that can be applied to training classification methods. ML approaches include supervised learning methods as support vector machines (SVMs), and random forests (RF), which can be implemented for classifying the healthcare data. The methods were trained on the ChatGPT-generated data that can be carefully validated and labelled with suitable classes related to the healthcare field. With this motivation, this article presents an automated healthcare data classification-using barnacles mating optimizer with a pyramid neural network (AHDC-BMOPNN) technique. The presented AHDC-BMOPNN technique examines the healthcare data effectually using an ML model with a feature selection process. Primarily, the AHDC-BMOPNN technique exploits min-max data normalization for scaling the input dataset. In addition, the butterfly optimization algorithm-based feature selection (BOA-FS) method is deployed for the selection of optimum feature subset. In this work, the PNN algorithm was utilized for the classification of medical data. Ultimately, the BMO-based hyperparameter tuning process takes place to boost the overall classifier results of the PNN technique. The empirical findings of the AHDC-BMOPNN approach was validated on ChatGPT generated dataset. The simulation values highlight that the AHDC-BMOPNN method and the diverse healthcare text data generated by ChatGPT enhance the ability to extract valuable insights and organize medical information effectively. 2024 34 45 2987 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJAACI.050203 https://www.americaspg.com/journal/31/article/2987 https://www.americaspg.com/storage/11720294691.pdf