XML‐LightGBMDroid: A self‐driven interactive mobile application utilizing explainable machine learning for breast cancer diagnosis

Abstract

AbstractNowadays, breast cancer detection and diagnosis are done using machine learning algorithms. It can enhance cancer understanding and help in treatment selection and diagnosis. But many reliable decision assistance systems have been developed as “black boxes,” or devices that conceal their internal workings from the user. In fact, this method's output is difficult to understand, which makes it difficult for doctors to use it. This study uses explainable machine learning to investigate a technique for more promptly and accurately predicting breast cancer. The data is obtained from Kaggle to generate a machine learning (ML) model that forecasts the occurrence of breast cancer and Shapley Additive exPlanations (SHAP) are used to interpret the model's forecasts. To forecast the development of this disease, explainable machine learning (XML) model based on gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM) is built. The investigation's findings show that the LightGBM is capable of a maximum accuracy of 99%. An explainable ML has been demonstrated here which may produce an explicit understanding of how models generate their predictions, which is critical in boosting the confidence and acceptance of cutting‐edge ML methods in oncology and healthcare in general. Finally, a mobile app is also developed, integrating the best model.