{"id":38975,"date":"2025-01-22T06:47:26","date_gmt":"2025-01-22T06:47:26","guid":{"rendered":"https:\/\/uomosul.edu.iq\/en\/computerscience\/?p=38975"},"modified":"2025-01-22T09:02:30","modified_gmt":"2025-01-22T09:02:30","slug":"1-114","status":"publish","type":"post","link":"https:\/\/uomosul.edu.iq\/en\/computerscience\/2025\/01\/22\/1-114\/","title":{"rendered":"Master\u2019s thesis by \u00a0Mohammed Faris Ali Ahmed"},"content":{"rendered":"<p>Discussion of the master\u2019s thesis in the College of Computer Science and Mathematics -Department of Statistics and Informatics Department entitled:<\/p>\n<p>Statistical Prediction of Thalassemia Behavior Using Radial Basis Function Network<\/p>\n<p>Master\u2019s thesis by \u00a0<strong>Mohammed Faris Ali Ahmed<\/strong><\/p>\n<p>supervised by \u00a0<strong>Assist. Prof. Dr. Hutheyfa Hazam Taha Majid<\/strong><\/p>\n<p>The thesis addressed the classification of thalassemia patients, a hereditary blood disease that leads to a decrease in the number and quality of red blood cells and iron accumulation, using machine learning techniques. The study was based on a dataset of 12 independent variables and 280 observations, divided into 149 cases of thalassemia intermedia and 131 cases of thalassemia major, collected from Al-Hadbaa Specialized Hospital for Blood Diseases and Bone Marrow Transplantation.<\/p>\n<p>The study addressed pre-processing the data to deal with missing values, and then implementing simulations of the models using the Python language via the Colab platform. Three classification models were applied, namely the radial basis function (RBF) network, the multilayer neural network (MLP), and the k-nearest neighbor (KNN) algorithm with the use of Manhattan and Jaccard distances.<\/p>\n<p>The thesis aims at early diagnosis and prediction of the disease to prevent future complications, as the problem of predicting thalassemia was studied because it is considered one of the most dangerous diseases at the present time. The objectives of this work are summarized as follows:<\/p>\n<p>(Identifying the risk factors for thalassemia that play a major role in predicting the disease as well as classifying thalassemia using artificial neural networks and statistical methods in addition to verifying the validity of the trained models using model performance metrics based on the measures of accuracy (Testing Accuracy), expected positivity (Precision), sensitivity (Sensitivity), specificity (Specificity), F1-score, and area under the curve (AUC).<\/p>\n<p>(Assist. Prof. Dr. Muthanna Sobhi Suleiman)\u00a0 President<\/p>\n<p>(Assist. Prof. Dr. Osama Bashir Shukr)\u00a0 Member<\/p>\n<p>(Dr. Omar Salem Ibrahim)\u00a0 Member<\/p>\n<p>(Assist. Prof. Dr. Hutheyfa Hazam Taha Majid )\u00a0 Member and supervisor<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Discussion of the master\u2019s thesis in the College of Computer Science and Mathematics -Department of Statistics and Informatics Department entitled: Statistical Prediction of Thalassemia Behavior Using Radial Basis Function Network Master\u2019s thesis by \u00a0Mohammed Faris Ali Ahmed supervised by \u00a0Assist. Prof. Dr. Hutheyfa Hazam Taha Majid The thesis addressed the classification of thalassemia patients, a hereditary blood disease that leads to a decrease in the number and quality of red blood cells and iron accumulation, using machine learning techniques. The study was based on a dataset of 12 independent variables and 280 observations, divided into 149 cases of thalassemia intermedia and <a href=\"https:\/\/uomosul.edu.iq\/en\/computerscience\/2025\/01\/22\/1-114\/\"> [Read More]<\/a><\/p>\n","protected":false},"author":24,"featured_media":38977,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-38975","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-viva"],"_links":{"self":[{"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/posts\/38975","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/users\/24"}],"replies":[{"embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/comments?post=38975"}],"version-history":[{"count":1,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/posts\/38975\/revisions"}],"predecessor-version":[{"id":38976,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/posts\/38975\/revisions\/38976"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/media\/38977"}],"wp:attachment":[{"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/media?parent=38975"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/categories?post=38975"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/uomosul.edu.iq\/en\/computerscience\/wp-json\/wp\/v2\/tags?post=38975"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}