Predictive Modeling for Breast Cancer Classification in the Context of
  Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable
  AI
Taminul Islam Md. Alif Sheakh Mst. Sazia Tahosin Most. Hasna Hena Shopnil Akash Yousef A. Bin Jardan Gezahign Fentahun Wondmie Hiba-Allah Nafidi Mohammed Bourhia
Breast cancer has rapidly increased in prevalence in recent years, making it
one of the leading causes of mortality worldwide. Among all cancers, it is by
far the most common. Diagnosing this illness manually requires significant time
and expertise. Since detecting breast cancer is a time-consuming process,
preventing its further spread can be aided by creating machine-based forecasts.
Machine learning and Explainable AI are crucial in classification as they not
only provide accurate predictions but also offer insights into how the model
arrives at its decisions, aiding in the understanding and trustworthiness of
the classification results. In this study, we evaluate and compare the
classification accuracy, precision, recall, and F-1 scores of five different
machine learning methods using a primary dataset (500 patients from Dhaka
Medical College Hospital). Five different supervised machine learning
techniques, including decision tree, random forest, logistic regression, naive
bayes, and XGBoost, have been used to achieve optimal results on our dataset.
Additionally, this study applied SHAP analysis to the XGBoost model to
interpret the model's predictions and understand the impact of each feature on
the model's output. We compared the accuracy with which several algorithms
classified the data, as well as contrasted with other literature in this field.
After final evaluation, this study found that XGBoost achieved the best model
accuracy, which is 97%.