An ensemble algorithm using quantum evolutionary optimization of weighted type-II fuzzy system and staged Pegasos Quantum Support Vector Classifier with multi-criteria decision making system for diagnosis and grading of breast cancer

Breast cancer is a life-threatening and consequential disease due to its invasive and proliferative trait, predominantly found in women. Early detection of the cancer is a significant contributor to improved mortality and hence is an area of keen focus for ongoing researches. However, developing a technique to diagnose the severity of the patients at an early stage is a challenging task. Manual diagnostic techniques are time-consuming and result in inaccurate diagnosis of breast cancer. Prompted by these facts, a quantum optimized rule-base generated automated framework is developed to cluster the data based on degree of criticality of the cancer patients and further classify it as benign or malignant utilizing probability of malignancy of the clusters along with assignment of grades of cancer. Firstly, after implementing data pre-processing step, significant features are selected using an integrated feature selection approach. An efficient weightage algorithm is proposed incorporating the knowledge of physicians and the benefits of regression analysis which thereby provides a novel approach for detection of breast cancer. A novel ensemble clustering and classification algorithm employing voting-based Weighted Interval Type-II Fuzzy Inference System and Staged Pegasos Quantum Support Vector Classifier is then developed basis the prioritization of clusters depicting the critical state of breast cancer. A grading approach is also proposed based on fuzzy linguistic multi-criteria decision making system. Finally, the research is validated on Wisconsin Breast Cancer dataset. The detailed implementation of the proposed integrated model is accomplished to establish its superiority over other existing models in the literature.