A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making

Abstract

Worldwide, the coronavirus has intensified the management problems of health services, significantly harming patients. Some of the most affected processes have been cancer patients’ prevention, diagnosis, and treatment. Breast cancer is the most affected, with more than 20 million cases and at least 10 million deaths by 2020. Various studies have been carried out to support the management of this disease globally. This paper presents a decision support strategy for health teams based on machine learning (ML) tools and explainability algorithms (XAI). The main methodological contributions are: first, the evaluation of different ML algorithms that allow classifying patients with and without cancer from the available dataset; and second, an ML methodology mixed with an XAI algorithm, which makes it possible to predict the disease and interpret the variables and how they affect the health of patients. The results show that first, the XGBoost Algorithm has a better predictive capacity, with an accuracy of 0.813 for the train data and 0.81 for the test data; and second, with the SHAP algorithm, it is possible to know the relevant variables and their level of significance in the prediction, and to quantify the impact on the clinical condition of the patients, which will allow health teams to offer early and personalized alerts for each patient.

Ensemble Approach Using k-Partitioned Isolation Forests for the Detection of Stock Market Manipulation

“…Adopting the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology in our investigation into the capabilities of an isolation forest ensemble for detecting stock market manipulation offers a structured, iterative, and comprehensive framework that significantly enhances the study's scientific rigor and practical applicability. The CRISP-DM methodology has been previously and successfully employed in other machine learning projects, as evidenced by the literature [21][22][23]. By meticulously following CRISP-DM's phases-from understanding the business problem and data to model evaluation and deployment-we ensure a deep alignment between our models and the real-world phenomenon of market manipulation.…”

A contemporary review of breast cancer risk factors and the role of artificial intelligence

“…A novel approach that integrates Machine Learning (ML) algorithms with Explainable Artificial Intelligence (XAI) has been recently developed to enhance the understanding and interpretation of predictions made by ML models. In the context of breast cancer research ( 95 ), introduced a Hybrid Algorithm combining ML and XAI techniques aimed at preventing breast cancer. This innovative methodology enables the identification and extraction of key risk factors, such as high-fat diets and breastfeeding habits, to accurately differentiate between patients with and without breast cancer among Indonesian women.…”

The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities

“…The SHAP method has been widely applied across various domains for interpreting machine learning models, including finance [89], healthcare [90], and environmental sciences [91], to provide interpretable explanations of complex machine learning models. In our study, we leverage the predictive capabilities of an XGBoost ML model and enhance its interpretability using SHAP.…”