aspg-21-1068-1791468694 20261008141134 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Neutrosophic Science IJNS 2692-6148 2690-6805 2022 18 3 Sundus Naji AL-Aziz Department of Mathematical Sciences, Faculty of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia Reem Atassi Faculty of Computer Information System, Higher Colleges of Technology, UAE Abd Al-Aziz Hosni El-Bagoury Higher Institute of Engineering and Technology, El-Mahala El-Kobra, Egypt. Data classification is the procedure of investigating structured or unstructured data and forming it into distinct classes depending upon file types, size, etc. It assist the organizations to derive important solutions based on the data and helps decision making process. The computational intelligence techniques such as neural computing, fuzzy logic, machine learning, etc. can be used to design effective data classification models. This study offers a Hybridization of Neutrosophic Logic with Quasi-Oppositional Chimp Optimization based Data Classification (HNLQOCO) model. The presented HNLQOCO algorithm aims to integrate the concepts of NL and QOCO algorithm for improved data classification outcomes. Besides, the QOCO algorithm is designed by incorporating the concepts of quasi oppositional based learning (QOBL) with traditional chimp optimization algorithm (COA). Here, the NL is applied to represent various kinds of knowledge and the QOCO algorithm is applied to tune the produced NS rules. The experimental result analysis of the HNLQOCO model is tested using three benchmark medical dataset. The obtained results reported the significant performance of the HNLQOCO model over the other methods. 2022 125 134 1068 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJNS.1803011 https://www.americaspg.com/journal/21/article/1068 https://www.americaspg.com/storage/5089.pdf