Mostafa Boroumandzadeh - Department of Computer Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.
Elham Parvinnia - Department of computer engineering, Shiraz branch, Islamic Azad university, Shiraz, Iran.
Reza Boostani - Biomedical Group‎, ‎CSE IT Department‎, ‎ECE Faculty‎, ‎Shiraz University‎, ‎Shiraz‎, ‎Iran
Sepideh Sefidbakht - Department of Radiology‎, ‎Medical imaging research center‎, ‎Shiraz University of Medical Sciences‎, ‎Shiraz‎, ‎Iran

Medical decision support systems (MDSS) are designed to assist physicians in making accurate decisions‎. ‎The required data by MDSS are collected from various resources such as physical examinations and electronic health records (EHR)‎. ‎In this paper‎, ‎an MDSS framework has been proposed to diagnose and classify breast cancer patients (DSS-BC)‎. ‎Medical texts reports (MTR) were embedded‎, ‎and essential feature vectors combined with EHR were extracted using principal component analysis (PCA)‎. ‎A new method based on a fuzzy min-max neural network with hyper box variable expansion coefficient (FMNN-HVEC) was used to determine the molecular subtypes‎, ‎and the feature vectors were clustered using deep clustering‎. ‎Also‎, ‎a new decision fusion algorithm called weighted Yager was proposed based on the F۱-Score for each class‎. ‎This algorithm proposed a mathematical decision fusion technique to determine the Breast Imaging-Reporting and Data System (BI-RADS) and molecular subtypes values with the accuracy of ۹۵.۱۲% and ۸۹.۵۶%‎, ‎respectively.

Decision support system‎, ‎Text mining‎, ‎Breast cancer‎, ‎BI-RADS‎, ‎Decision fusion