19 August 2026
SCIE-Indexed Q1 Publication by Prof. Dr Cemalettin KUBAT
The study, co-authored by Prof. Dr. Cemalettin Kubat, was published in the Q1 category journal Computers & Industrial Engineering. In the research, machine learning algorithms used in thyroid cancer prediction were improved through hyperparameter optimization, resulting in more reliable, explainable, and repeatable results.
A study co-authored by Prof. Dr. Cemalettin Kubat, a faculty member in the Department of Industrial Engineering at Istanbul Gelisim University (IGU) Faculty of Engineering and Architecture, has been published in the internationally respected Q1 category journal “Computers & Industrial Engineering.”
The article, titled “Optimizing Binary Classification Algorithms Through Hyperparameter Tuning: A Case Study on Thyroid Cancer Prediction,” prepared by Prof. Dr. Cemalettin Kubat, was published in the 219th volume of the journal, which is indexed in SCI-Expanded (SCI-E) and Scopus.
The study examines hyperparameter optimization methods to improve the performance of binary classification algorithms used in thyroid cancer prediction. The research, which evaluated different machine learning models using real patient data, yielded significant results aimed at developing a more reliable, explainable, and repeatable prediction approach.
This study, contributing to artificial intelligence and machine learning applications in the healthcare field, once again demonstrates the importance of interdisciplinary research in the early and accurate diagnosis of diseases.
We congratulate Prof. Dr. Cemalettin Kubat and wish him continued success in his academic endeavors.