Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients - A Supervised Machine Learning Approach with Explainable AI

Aug 4, 2026

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Breast cancer prevalence has increased rapidly in recent years, making it a major cause of mortality worldwide and creating strong demand for faster, more reliable diagnosis. Manual assessment requires substantial time and expertise, so machine-based forecasting can help limit disease spread. This study evaluates and compares five supervised learning methods for breast cancer classification using a primary dataset of 500 patients from Dhaka Medical College Hospital. It further applies SHAP to the XGBoost model to interpret predictions and quantify feature impacts, supporting accuracy (reported up to 97%).

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages33

Reading Time1 h 23 min

AccessFree to download

Abstract

1. Introduction

Breast cancer background and symptoms

Risk factors and screening challenges

Role of machine learning and explainability

Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable AI Taminul Islam 1, Md. Alif Sheakh 2, Mst. Sazia Tahosin 2 , Most. Hasna Hena 2, Shopnil Akash 3,Yousef A. Bin Jardan 4, Gezahign FentahunWondmie 5,4*, Hiba-Allah Nafidi 6, Mohammed Bourhia 7* 1. School of Computing, Southern Illinois University Carbondale, IL, United States 2. Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh 3. Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, Bangladesh. 4. Department of Pharmaceutics, College of Pharmacy, King Saud University, P.O. Box 11451, Riyadh, Saudi Arabia. 5. Department of Biology, Bahir Dar University, P.O. Box 79, Bahir Dar, Ethiopia. 6. Department of Food Science, Faculty of Agricultural and Food Sciences, Laval University, 2325Quebec City, QC G1V 0A6, Canada. 7. Department of Chemistry and Biochemistry, Faculty of Medicine and Pharmacy, Ibn Zohr University, Laayoune 70000, Morocco. Corresponding Author: [resercherfent@gmail.com](resercherfent@gmail.com) (GFW) Abstract 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 timeconsuming 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% . Keywords: Breast cancer prediction; Machine learning; Cancer prediction; Hyperparameter tuning, Explainable AI. 1. Introduction Breast cancer begins when some cells in the breast start to grow uncontrollably, forming a mass called a tumor 1. Abreast cancer diagnosis typically falls into one of two main categories – benign (non-cancerous) or malignant (cancerous) . Malignant tumors are dangerous as they can spread to distant sites in the body through the bloodstream or lymph system, a process known as metastasis 2,3 . Figure 1 (a) illustrates the distinction between benign and malignant tumors in terms of the normal cells and tumor cells, and (b) shows the benign and malignant masses. Benign tumors generally stay localized in one area and do not metastasize. Breast cancer manifests through several symptoms-a noticeable lump or mass in the breast, changes in breast size or shape compared to the other breast, alterations in the skin overlying the breast like dimpling or puckering, newly inverted nipple, redness or scaliness of breast skin, breast pain, and nipple discharge other than breast milk 4. Fig. 1. Visualization of breast cancer: (a) benign and malignant tumor cells,(b) benign and malignant masses Breast cancer is the second largest killer of women after cardiovascular disease. A

FAQs

Which supervised machine learning methods were used for breast cancer classification?

The study uses decision tree, random forest, logistic regression, naive Bayes, and XGBoost on the dataset from Dhaka Medical College Hospital.

How was model interpretability achieved in this study?

SHAP analysis was applied to the XGBoost model to interpret predictions and assess how each feature influences the model output.

What model performed best and what accuracy was reported?

After evaluation, XGBoost achieved the best model accuracy, reported as 97%.

Aug 4, 2026

Upload by Aria