Subsequent researchers and medical teams have reported that cancer is a diverse illness with several subtypes. Such cancer-causing illnesses are to be diagnosed at an early stage and the prognosis leads to various research among the cancer commu-nity. Because of the importance of placing those with cancer as high- or low-risk, several research teams in the biomedicaland bioinformatics areas have investigated the use of machine-learning approaches. The ultimate goal of the research projectis to create an algorithm for multi-criteria ensemble learning. to detect breast cancer. Reducing the amount of time requiredto process the classifier is the ultimate goal of the study, which will increase accuracy. The system takes the DICOM imagesand pre-processes them on applying SMOTE and data augmentation. The data is then analysed for feature extraction througha convolution neural network. The optimised DICOM images are fed as input to ensemble learning algorithms framed overAda-boost(CNNbAB), gradient-boost decision tree (CNNbGBDT) and random forest (CCNbRF) algorithms. The reasonbehind the choice of the algorithm is to simplify the processing and time complexity of the time without compromisingthe accuracy of CNN. It is observed that the accuracy of the system is 98.65% which is found to be a better value than theexisting system for cancer diagnosis. (PDF) Ensemble Machine Learning Algorithms for Precision Breast Cancer Diagnosis: A Multi-criteria Evaluation Approach. Available from: https://www.researchgate.net/publication/388892101_Ensemble_Machine_Learning_Algorithms_for_Precision_Breast_Cancer_Diagnosis_A_Multi-criteria_Evaluation_Approach#fullTextFileContent [accessed Oct 08 2025].