Could fuzzy logic be used to predict if the tumor of a patient is benign or malignant based on the fine needle aspiration test?
With this question in mind, Institute of Information and Computing Sciences faculty member Asst. Prof. Mylene J. Domingo pursued with unparalleled vigor her study resulting in a research paper that bagged the Best Paper award in the 2020 2nd International Conference on Big Data Engineering and Technology (BDET 2020) and the 2020 International Conference on Frontiers of Computers and Communication Engineering (FCCE2O20) held on January 4, 2020 at the Nanyang Technological University, Singapore.
Considered as a premier conference in the field, the BDET and FCCE provide a highly competitive forum for reporting the latest developments in the research and application of Big Data Engineering and Technology and Frontiers of Computers and Communication Engineering.
In Domingo’s study titled “Fuzzy Rule Based Inference System in Patient Diagnosis of Breast Tumor in Fine Needle Aspiration,” it was revealed that: “Fuzzy logic has an accuracy of 96.93%, sensitivity and specificity of 97.06%. Thus, fuzzy logic is good for predicting the class of breast tumor.” Domingo further explained that the “Mamdani fuzzy inference method is used in the development of the fuzzy model, and the rules are generated based on the experts’ domain. The value of each selected attribute is then inputted in the Rule Viewer to determine the class of breast cancer tumor using CRISP input.”
Domingo, who once served as Institute Secretary of IICS, teaches Data Analytics and Programming. She holds a Master’s Degree in Information Technology and has conducted research studies in the field of Data Mining.
