Interpretability and Explainability of Logistic Regression Model for Breast Cancer Detection

Emina Tahirović, Senka Krivić

2023

Abstract

Artificial Intelligence techniques are widely used for medical purposes nowadays. One of the crucial applications is cancer detection. Due to the sensitivity of such applications, medical workers and patients interacting with the system must get a reliable, transparent, and explainable output. Therefore, this paper examines the interpretability and explainability of the Logistic Regression Model (LRM) for breast cancer detection. We analyze the accuracy and transparency of the LRM model. Additionally, we propose an NLP-based interface with a model interpretability summary and a contrastive explanation for users. Together with textual explanations, we provide a visual aid for medical practitioners to understand the decision-making process better.

Paper Citation

in Harvard Style

Tahirović E. and Krivić S. (2023). Interpretability and Explainability of Logistic Regression Model for Breast Cancer Detection. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 161-168. DOI: 10.5220/0011627600003393

in Bibtex Style

@conference{icaart23,
author={Emina Tahirović and Senka Krivić},
title={Interpretability and Explainability of Logistic Regression Model for Breast Cancer Detection},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={161-168},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011627600003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Interpretability and Explainability of Logistic Regression Model for Breast Cancer Detection
SN - 978-989-758-623-1
AU - Tahirović E.
AU - Krivić S.
PY - 2023
SP - 161
EP - 168
DO - 10.5220/0011627600003393