Leveraging Interpretable Models and Low Complexity Models for Early Breast Cancer Diagnosis: A Machine Learning Approach

Akampurira Paul, Atuhe Aaron, Mugisha Brian, Kyomuhangi Rosette, Alitweza Joshua, Ainomugisha Maxima and Tumuramye Juliana
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Akampurira Paul: Kampala International University and Mbarara University of Science and Technology
Atuhe Aaron: Kampala International University and Mbarara University of Science and Technology
Mugisha Brian: Kampala International University and Mbarara University of Science and Technology
Kyomuhangi Rosette: Kampala International University and Mbarara University of Science and Technology
Alitweza Joshua: Kampala International University and Mbarara University of Science and Technology
Ainomugisha Maxima: Kampala International University and Mbarara University of Science and Technology
Tumuramye Juliana: Kampala International University and Mbarara University of Science and Technology

International Journal of Research and Innovation in Applied Science, 2024, vol. 9, issue 11, 508-527

Abstract: A crucial component of women’s healthcare is the identification of breast cancer, which necessitates precise and understandable predictive models. Even though machine learning has great potential, obstacles including unbalanced datasets, computing complexity, and interpretability impede advancement. To overcome these obstacles, we used a novel strategy in this study that concentrated on lightweight and interpretable models. In particular, we use decision trees, K-Nearest Neighbors (K-NN), and Support Vector Machines (SVM) for breast cancer diagnosis using logistic regression as a meta-learner. Our study uses the Wisconsin Breast Cancer (WBC) dataset, a gold standard in breast cancer research, to demonstrate the efficacy of this ensemble technique. Through the utilization of base models’ simplicity and the interpretability of logistic regression, we are able to achieve diagnosis transparency and accuracy, which helps physicians make well-informed decisions.

Date: 2024
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