Mohammad Zahaby; Mostafa Boroumandzadeh; Iman Makhdoom
Abstract
Breast cancer is one of the most prevalent cancers among women and remains a leading cause of cancer-related mortality. Mammography is the primary imaging modality for the early detection of breast tumors. Providing timely and highly accurate diagnoses is a top priority for physicians ...
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Breast cancer is one of the most prevalent cancers among women and remains a leading cause of cancer-related mortality. Mammography is the primary imaging modality for the early detection of breast tumors. Providing timely and highly accurate diagnoses is a top priority for physicians and healthcare providers in the management of critical illnesses. This paper presents a Medical Decision Support System (MDSS) that utilizes Yager’s rule of combination to classify and diagnose breast cancer patients by integrating information from multiple data sources. Medical text reports (MTR) and key feature vectors extracted from electronic health records (EHR) were reduced using Principal Component Analysis (PCA) and then classified using Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), and Support Vector Machines (SVM). Medical images were preprocessed and classified using a U-Net model. A novel decision fusion algorithm, called weighted Yager, was introduced to determine the Breast Imaging-Reporting and Data System (BI-RADS) categories, taking into account the accuracy of each class in each classifier as evidence. The performance of the proposed system was evaluated based on standard metrics including accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. The proposed system achieved the highest accuracy of 96.23\%, outperforming individual classifiers (CNN: 86.37%, MLP: 92.11%, SVM: 87.92%, U-Net: 92.97%, and Yager: 93.49%). The weighted Yager fusion method yielded the best performance with an accuracy of 96.23%, sensitivity of 98.80%, specificity of 85.90%, PPV of 86.21%, NPV of 97.82%, and F1-score of 85.87%. These findings demonstrate that integrating decisions from multiple classifiers significantly improves diagnostic accuracy and robustness.
Mostafa Boroumandzadeh; Elham Parvinnia; Reza Boostani; Sepideh Sefidbakht
Abstract
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 ...
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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 F1-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 95.12% and 89.56%, respectively.
