A higher technical diploma research project in Electronic Management Technologies was discussed at the Administrative Technical College / Mosul, Northern Technical University, by the student Sarab Mohsen Habash Bairam. The research was entitled:
“A Clinical Decision Support System Using Intelligent Technologies for Heart Disease Diagnosis.”
The discussion was held on Thursday morning, August 27, 2026, at 9:00 a.m. in Al-Hikma Hall at the college.
The research aimed to employ artificial intelligence technologies, particularly machine learning techniques, to develop a clinical decision support system that contributes to the early and accurate diagnosis of heart disease. It also sought to utilize the capabilities of these technologies in analyzing clinical data and identifying complex patterns and relationships in order to support the diagnostic decision-making process.
The research relied on the Cleveland dataset available in the UCI Repository, consisting of 303 cases described by 13 clinical features. The data underwent preprocessing and class balancing using the SMOTE technique. Three algorithms were trained: Logistic Regression (LR), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). In addition, a software model with an interactive interface was developed in the MATLAB environment, and the system was tested on new patient cases.
The results showed that the Logistic Regression algorithm achieved the best performance, with an accuracy of 96.1%, sensitivity of 94.9%, and an area under the curve (AUC) of 0.987. It was followed by the K-Nearest Neighbors algorithm, which achieved an accuracy of 94.4%, while the Support Vector Machine recorded an accuracy of 59.1%. The results also highlighted the importance of combining the outputs of different models and using an ensemble voting mechanism to support diagnostic decision-making and reduce the impact of the lower-performing model.
The examination committee consisted of:
- Asst. Prof. Dr. Ahmed Sabeeh Yousif – Chairman.
- Asst. Prof. Ahmed Hamid Saleh – Member.
- Asst. Prof. Dr. Ridwan Yousif Sadiq – Member and Supervisor.
