International Journal on Science and Technology

E-ISSN: 2229-7677   •   Impact Factor: 9.88

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Call for Paper Volume 17 Issue 2 April-June 2026 Submit your research before last 3 days of June to publish your research paper in the issue of April-June.
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AI-Driven Decision Support System for Early Breast Cancer Detection

Author(s) Dr. Jothi G G, Dr. Nivetha S S, Dr. Hannah Inbarani H H
Country India
Abstract Breast cancer is one of the leading cancer types among women worldwide. The early and accurate diagnosis of breast cancer is crucial to important to increase the patient's life time. In this research a GUI-based breast cancer prediction system using Artificial Intelligence techniques to classify breast cancer as benign or malignant. The proposed model utilizes three different models, namely, Artificial Neural Network, Logistic Regression, and Random Forest, to evaluate the breast cancer prediction system. The model is trained with the Breast Cancer Wisconsin (Diagnostic) dataset, which consists of 30 numerical features derived from cell nucleus characteristics. The model's performance is assessed using standard evaluation metrics, including accuracy, precision, recall, F1 score, ROC curves, and confusion matrices. In addition to that, the trained models are integrated into a graphical user interface, enabling user interaction, real-time prediction, model comparison, and visualization of evaluation results. The empirical results show that a deep learning-based ANN model achieves superior performance in the accuracy of 97% when compared to other models. The proposed GUI-based breast cancer prediction system provides an efficient, interpretable, and user-friendly decision support tool for early breast cancer detection.
Keywords Breast Cancer, Machine Learning, Deep Learning, ANN, Graphical User Interface
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-05-05
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