Abstract :

Early Detection Of Breast Tumors Plays A Crucial Role In Improving Treatment Outcomes And Reducing Mortality Rates Among Women Worldwide. This Study Presents An Intelligent Machine Learning–based Approach Designed To Recognize Breast Tumors At An Early Stage Using Clinical And Imaging Data. The Proposed System Incorporates Data Preprocessing, Feature Selection, And Classification Techniques To Enhance Diagnostic Accuracy And Minimize False Predictions. Multiple Machine Learning Models, Including Support Vector Machines, Random Forests, And Logistic Regression, Are Evaluated To Determine The Most Effective Classifier For Tumor Identification. The System Learns Patterns From Labeled Datasets And Accurately Distinguishes Between Benign And Malignant Cases. Experimental Results Demonstrate A Significant Improvement In Prediction Performance, Offering Fast And Reliable Assessment Support For Healthcare Professionals. This Approach Aims To Assist Radiologists In Decision-making, Reduce Diagnostic Errors, And Promote Timely Medical Intervention, Ultimately Contributing To Improved Patient Care And Early Breast Cancer Management. Keywords: Breast Cancer Detection, Early Tumor Recognition, Machine Learning, Medical Image Analysis, Classification Models, Intelligent Healthcare, Deep Learning, Diagnostic Accuracy, Feature Extraction, CAD Systems.

Published:

11-12-2025

Issue:

Vol. 25 No. 12 (2025)

Page Nos:

157-162

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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