10th WorldS4 2026

Authors - Jeanne Roux Ngo Bilong, Python Ndekou Tandong Paul, Bakary Kone, Dethie Dione, Ibrahima Toure, Boris Sourou ZANNOU, Hamidou Dathe, Mamadou Diarra, Olga Ngangmo Kengni, Mamadou Thiam, Cheikh Amed Diloma Gabriel Traore
Abstract - Breast cancer is a non-communicable disease that causes thousands of deaths each year worldwide. Early detection of breast cancer in women is a public health priority in developing countries. Based on data collected from patients’ breasts, machine learning models can help predict the risk of developing breast cancer.We used three machine learning algorithms (SVM, decision tree, and random forest) for predicting the risk of developing breast cancer, taking into account the physiological factors of breasts. Data collected from 568 patients was used to train the machine learning algorithms. An evaluation of the performance of the three algorithms showed that the random forest algorithm had the highest F1 score, which led to the selection of this algorithm for creating a computer application to diagnose breast cancer risk. The results of this algorithm show 97% accuracy with 94% recall in predicting high breast cancer risk and an F1 score of 96%. The result obtained indicates the model’s excellent ability to correctly predict high cancer risk across the entire risk probability spectrum. The good performance of the model using the Random Forest algorithm could be useful as first-level medical support for breast cancer screening. The developed model is capable of providing the probability of the risk of developing breast cancer for each female patient.