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Engineering and Technology OPEN ACCESS

EVALUATING MACHINE LEARNING ALGORITHMS FOR BREAST CANCER DETECTION: A STUDY ON ACCURACY AND PREDICTIVE PERFORMANCE

Md Al-Imran
College of Graduate and Professional Studies Trine University, USA
Salma Akter
Department of Public Administration, Gannon University, Erie, PA, USA
Md Abu Sufian Mozumder
College of Business, Westcliff University, Irvine, California, USA
Rowsan Jahan Bhuiyan
Master of Science in Information Technology, Washington University of Science and Technology, USA
Tauhedur Rahman
Dahlkemper School of Business, Gannon University, USA
Md Jamil Ahmmed
Department of Information Technology Project Management, Business Analytics, St. Francis College, USA
Md Nazmul Hossain Mir
Master of Science in Information Technology, Washington University of Science and Technology, USA
Md Amit Hasan
Master of Science in Information Technology, Washington University of Science and Technology, USA
Ashim Chandra Das
Master of Science in Information Technology, Washington University of Science and Technology, USA
Md. Emran Hossen
Department of Science in Biomedical Engineering, Gannon University, USA
DOI Crossref DOI 10.37547/tajet/Volume06Issue09-04
tajet 2024
VOL. 6 / NO. 09 SEPTEMBER
FULL TEXT PP. 22-33
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VOLUME 6
ISSUE 09
YEAR 2024
PAGES 22-33
RESEARCH OVERVIEW

Abstract

This study evaluates several machine learning algorithms—Support Vector Machine (SVM), Random Forest, Logistic Regression, Decision Tree (C4.5), and k-Nearest Neighbors (KNN)—for breast cancer detection using the Breast Cancer Wisconsin Diagnostic dataset. We implemented comprehensive pre-processing and model evaluation with Scikit-learn in Python. Our findings show that SVM achieved the highest accuracy, with 99.9% on the training set and 98.50% on the testing set, indicating superior performance in handling high-dimensional data. Random Forest also performed well, with accuracies of 98.5% and 98.20%, respectively. Logistic Regression and Decision Tree models provided reliable predictions when tuned, while KNN was less effective. SVM and Random Forest are recommended for clinical decision support systems due to their high accuracy and robustness.

RESEARCH TOPICS

Keywords

Accuracy rates Performance analysis Confusion matrix

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