Comparative Analysis of Machine Learning Algorithms for Predicting Breast Cancer Diagnosis

• Author(s): Durowade Adeyemi Nathaniel ; Ajayi Oyewole ; Ayobami Samuel O ; Sanni Bello ; Saheed Ajibade
• Paper ID: 1711021
• Page: 45-52
• Published Date: 06-10-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 4 October-2025

Abstract

This study conducts a comparative analysis of three prominent machine learning models—Logistic Regression, Random Forest, and Support Vector Machine (SVM),—for the classification of breast cancer. The data used for this study is collected from the records of incoming patients of breast cancer in Ekiti State University Teaching Hospital, Ekiti State, Nigeria. The analysis emphasizes the significance of certain features, particularly the worst measurements of texture, radius, and area, in distinguishing between benign and malignant tumors. Key features such as `area worst`, `radius worst`, and `texture worst` demonstrate high phi values, indicating their strong association with the class labels. This suggests that extreme values of these measurements are crucial in identifying malignancies. Among the evaluated models, the Random Forest model exhibits the highest accuracy, as validated by cross-validation techniques. The selection of `mtry = 2` as the optimal parameter underscores the importance of choosing the appropriate number of features at each split to maximize model performance. The model's reliability is further confirmed by confusion matrices, which show high sensitivity and specificity, critical for minimizing false negatives and positives in medical diagnoses. This study highlights the importance of feature importance analysis in medical data classification, revealing that focusing on key diagnostic indicators can enhance model interpretability and assist medical professionals. Future research could explore additional feature selection methods and classifiers to further improve the robustness and accuracy of breast cancer classification models. The findings underscore the Random Forest model as a highly effective tool for breast cancer diagnosis, supporting its integration into clinical workflows for improved patient outcomes.

Keywords

Breast Cancer, Machine Learning, Logistic Regression, Random Forest, Support Vector Machine

Citations

IRE Journals:
Durowade Adeyemi Nathaniel , Ajayi Oyewole , Ayobami Samuel O , Sanni Bello , Saheed Ajibade "Comparative Analysis of Machine Learning Algorithms for Predicting Breast Cancer Diagnosis" Iconic Research And Engineering Journals Volume 9 Issue 4 2025 Page 45-52

IEEE:
Durowade Adeyemi Nathaniel , Ajayi Oyewole , Ayobami Samuel O , Sanni Bello , Saheed Ajibade "Comparative Analysis of Machine Learning Algorithms for Predicting Breast Cancer Diagnosis" Iconic Research And Engineering Journals, 9(4)