Tikrit Journal of Pure Science Vol. 28 (1) 2023 82 Tikrit Journal of Pure Science ISSN: 1813 – 1662 (Print) --- E-ISSN: 2415 – 1726 (Online) Journal Homepage: http://tjps.tu.edu.iq/index.php/j An Intelligent Gestational Diabetes Mellitus Recognition System Using Machine Learning Algorithms Rasool Jader, Sadegh Aminifar Computer Science, Faculty of Science, Soran University, Erbil, Iraq https://doi.org/10.25130/tjps.v28i1.1269 A r t i c l e i n f o. Article history: -Received: 26 / 7 / 2022 -Accepted: 28 / 9 / 2022 -Available online: 20 / 2 / 2023 Keywords: Artificial Intelligence, Machine Learning, Clustering, Classification, Gestational Diabetes. Corresponding Author: Name: Rasool Jader E-mail: rasool.jader@gmail.com s.aminifar@yahoo.com Tel: ©2022 COLLEGE OF SCIENCE, TIKRIT UNIVERSITY. THIS IS AN OPEN ACCESS ARTICLE UNDER THE CC BY LICENSE http://creativecommons.org/licenses/by/4.0/ ABSTRACT Diabetes mellitus is also called gestational diabetes when a woman has high blood sugar while she is pregnant. It can show up at any time during pregnancy and cause problems for the mother and baby during or after the pregnancy. If the risks are found and dealt with as soon as possible, there is a chance that they can be reduced. The healthcare system is one of the many parts of our daily lives that are being rethought thanks to the creation of intelligent systems by machine learning algorithms. In this article, a hybrid prediction model is suggested as a way to find out if a woman has gestational diabetes. In the recommended model, the amount of data is reduce by using the K-means clustering method. Predictions are made using a number of classification methods, such as decision tree, random forests, SVM, KNN, logistic regression, and naive bayes. The results show that accuracy goes up when clustering and classification are used together. 1. Introduction Diabetes mellitus during pregnancy, also known as gestational diabetes, is one of the most frequent complications that may arise, affecting about one in every six live births globally [4]. Any degree of glucose inability, with the onset of symptoms occurring during pregnancy, is the definition of this condition. Conway at 2012 [9], discussed that GD may strike at any point during pregnancy, producing complications for the mother and baby throughout pregnancy and after the baby is born. If the risks are recognized and mitigated at an early stage, the potential consequences will be reduced. Algorithms for machine learning are being used in the creation of intelligent systems, which are bringing about changes in every facet of our life. By automating tasks that would normally be carried out by humans, artificial intelligence makes the lives of patients, clinicians, and hospital managers simpler. The current goals for AI include reducing the number of incorrect diagnoses of diabetes and developing more effective medical treatment methods [8]. These days, healthcare systems create vast volumes of data; thus, more complex systems are reliant on this data in order to construct more precise models. The purpose of this work is to attempt to construct a model that can examine both current and historical instances of diabetes in order to forecast and diagnose future cases using machine learning techniques, which may be of use to both patients and hospital administrators. And we attempted to determine, via the use of machine learning algorithms, which model has the highest degree of precision with the least amount of mistakes. In the Iraqi Kurdistan Region, we attempted to work with a data set that was obtained in both governmental and private labs. It is important to note that this was our goal. The information that we have gathered consists of the subjects' ages, as well as their weights, heights, gestational numbers, family medical history, and the results of their diabetes tests. This information reveals at what ages and in what circumstances pregnant women are more likely to develop gestational diabetes. The remains of this