Breast Cancer Detection Analysis Using Different Machine Learning Techniques: South Iraq Case Study

Contemporary oncology has seen a growing interest in digital technologies, whose integration with extensive healthcare and clinical data has raised new aspirations in managing patient profiles and organizing treatment plans. Among the commonly used digital technologies are Machine Learning (ML) methods that can perform many tasks, such as prediction, classification, and description, based on previously stored big data with high precision and speed. This study aims to develop a predictive ML model for early prediction of breast cancer based on a set of medically categorized risk factors. The locally collected database contained 415 instances from Al-Sadr Teaching Hospital in Basrah, Iraq, 219 (53%) of which were breast cancer patients, whereas 196 (47%) of them were control, respectively non-patients. It trained seven machine learning methods, namely Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Logical Regression (LR), Multinominal Naïve Bayes (NB), and Gaussian NB. The dataset was cleaned and balanced before being used. The results proved the superiority of the Decision Tree model with 96% accuracy, 96% sensitivity, and 96% specificity, the Random Forest model with 94% accuracy, 100% sensitivity, and 87% specificity, and SVM model with 92% accuracy, 96% sensitivity, and 87% specificity, respectively. Other models gave diverging results. The current study concluded that modern technologies should be employed to raise awareness and control diseases. The need to adopt Electronic Health Records (EHR) to ensure the integration of clinical data of different types recorded over time for patients contributes to building accurate and reliable prediction models.