A Way to Design Soft Computing Approaches to Create Models for Cloud-Based System to Detect Breast Cancer in Early Stage

Developing countries are still struggling to meet the urgent need for improvements in the healthcare industry. Breast cancer is the most common health issue that affects women. Numerous studies have continuously shown that the likelihood of surviving this illness is greatly increased with early identification. With two separate models-BCP- TIF and BCP-SVM-this paper presents a unique method for cloud-based intelligent breast tumour prediction utilising Type-l Fuzzy Logic and support vector machine algorithms (BCP-TIFSVM). The BCP-TIF-SVM system combines two well-known soft computing methods to provide an expert system that can accurately identify the kind and stage of breast cancer that an individual has. This expert system provides important details on the degree of the patient's illness by elaborating on the cancer's severity. Notably, when it comes to breast cancer diagnosis, the BCP-SVM model outperforms the BCP- TIF model in terms of accuracy. Extensive computations and analyses carried out in this research demonstrate that BCP-SVM performs better than BCP- TIF, with a precision percentage of 97.06%, while BCP- TIF manages a still respectable accuracy of 96.56%. The study concludes by highlighting the advantages of BCP-SVM over BCP- TIF in terms of breast cancer diagnosis and its better accuracy.