[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121445-en":3,"doc-seo-121445-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121445,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","A Comparative Analysis of ChatGPT and Traditional Machine Learning Algorithms on Real-World Data","Rapid advances in computer technologies have accelerated the adoption of artificial intelligence for automating tasks, with natural language processing systems such as ChatGPT increasingly used as analytic tools. Despite growing interest, performance concerns remain around accuracy, processing time, and reliability versus traditional coding-based machine learning algorithms. This study compares ChatGPT’s intelligent response generation across datasets and time intervals using the same account, evaluating accuracy, speed, and variability across diverse sources. Fifteen algorithms are tested on healthcare and education datasets, showing competitive accuracy but greater variability and slower processing, with case metrics reported and recommendations proposed.","Original Article  \nA Comparative Analysis of ChatGPT and Traditional Machine Learning Algorithms on Real-World Data  \nBnar Kamaran Arif a, b * , Aso M. Aladdin a, c   \na Computer Science Department, College of Science, Charmo University, Chamchamal, Iraq.  \nb Information Technology Department, College of Informatics, Sulaimani Polytechnic University, Sulaymaniyah, Iraq.  \nc Information Technology Department, Tishk International University, Sulaymaniyah, Iraq.  \nSubmitted: 17 May 2025  \nRevised: 10 June 2025  \nAccepted: 18 August 2025  \n* Corresponding Author:  \n[bnar.kamaran@chu.edu.iq](bnar.kamaran@chu.edu.iq)  \n[Keywords:](Keywords: ChatGPT)[ ChatGPT](Keywords: ChatGPT), Algorithm, Machine learning, Accuracy, Time processing.  \nHow to cite this paper: B. K. Arif, A. M. Aladdin,” A Comparative Analysis of ChatGPT and Traditional Machine Learning Algorithms on Real-World Data”, KJAR, vol. 10, no. 2, pp: 93-118, Dec 2025, doi:  \n10.24017/science.2025.2.8  \nCopyright: © 2025 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BYNC-ND 4.0)  \nAbstract: The rapid growth of computer-based technologies has transformed many sectors, with artificial intelligence playing a key role in automating tasks previously performed by humans. In this context, natural language processing models such as chatbots, including Chat Generative Pre-Trained Transformer (ChatGPT), are increasingly being used as analytical tools alongside traditional machine learning algorithms. However, despite these advancements, concerns remain regarding the accuracy, processing time, and overall reliability of ChatGPT compared to traditional coding-based machine learning algorithms. This study provides a comparative evaluation of ChatGPT’s ability to generate intelligent responses. It focuses on three key aspects: accuracy across various datasets at different time intervals using the same account, performance relative to traditional machine learning algorithms in terms of accuracy, and the variability of ChatGPT’s results across diverse data sources. To address these concerns, 15 algorithms were tested against ChatGPT. Tests were done at four different time intervals using healthcare and education datasets. ChatGPT showed competitive accuracy but had more variability and slower processing. As a result, this study highlights notable performance limitations for ChatGPT. For instance, in the heart disease dataset, the Random Forest model achieved an accuracy of 0.672 in 0.012 seconds, whereas the average performance of ChatGPT was 0.608 with a processing time of 0.274 seconds. In comparison, the traditional Gradient Boosting Machine model attained an accuracy of 0.623 in 0.124 seconds, while ChatGPT recorded an accuracy of 0.589 in 1.019 seconds. Finally, this study draws specific conclusions based on the results and offers recommendations for future research.  \n1. Introduction  \nThe rapid progression of computer-based information technology has had a significant impact on how many facets of human life are changing. The most recent technical innovation brought about by the accelerated development of technology is artificial intelligence (AI) [1-3] . AI is a widely used technology in modern application development, as it allows computers to perform many tasks that humans can do. This facilitates individuals in addressing their diverse needs more effectively [4, 5] . Chat Generative Pre-Trained Transformer (ChatGPT) is a natural language processing (NLP) technology and AI language chatbot that is driven by GPT, which is a series of advanced language models created by  \nOpenAI [6] . These models are designed to understand and produce text that closely resembles human language. The core of GPT is built on the transformer architecture, which uses a technique called selfattention to efficiently process and generate text sequences, allowing it to handle complex language tasks with impressive acc","cbCaifq3EE2h26LE","https://ap.wps.com/l/cbCaifq3EE2h26LE","pdf",1766198,1,26,"English","en",105,"# Introduction\n# Abstract\n# Comparative Evaluation Method\n## Accuracy Across Datasets and Time Intervals\n## Performance vs Traditional Algorithms\n## Variability Across Data Sources\n# Results and Discussion\n## Case Examples: Heart Disease Dataset\n# Conclusions and Future Recommendations","[{\"question\":\"What is the main goal of this comparative study?\",\"answer\":\"To evaluate how well ChatGPT generates intelligent responses compared with traditional machine learning algorithms on real-world datasets, focusing on accuracy, processing time, and result variability.\"},{\"question\":\"How is ChatGPT evaluated in relation to traditional algorithms?\",\"answer\":\"Fifteen algorithms are tested against ChatGPT, with experiments run at four different time intervals using healthcare and education datasets to compare accuracy and performance speed.\"},{\"question\":\"What key performance limitations does the study find for ChatGPT?\",\"answer\":\"ChatGPT shows competitive accuracy, but it has more variability in results and slower processing time compared with several traditional models.\"}]","A Comparative Analysis of ChatGPT and Traditional Machine Learning Algorithms on Real-World Data | 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is the main goal of this comparative study?","Question",{"text":75,"@type":76},"To evaluate how well ChatGPT generates intelligent responses compared with traditional machine learning algorithms on real-world datasets, focusing on accuracy, processing time, and result variability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is ChatGPT evaluated in relation to traditional algorithms?",{"text":80,"@type":76},"Fifteen algorithms are tested against ChatGPT, with experiments run at four different time intervals using healthcare and education datasets to compare accuracy and performance speed.",{"name":82,"@type":73,"acceptedAnswer":83},"What key performance limitations does the study find for ChatGPT?",{"text":84,"@type":76},"ChatGPT shows competitive accuracy, but it has more variability in results and slower processing time compared with several traditional 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