Development of a Cost-Effective Intelligent Clinical Decision Support System for Breast Cancer Early Diagnosis and Triage

Abstract

Women population screening using mammography has dramatically reduced breast cancer rates worldwide. Nowadays, in many countries, the prevention of breast cancer policy is based on frequent and repeated mammographies, followed by breast ultrasound and if necessary, by histological examination in the biological material of the biopsy. However, evaluating mammography findings is considered as a difficult process which can properly performed only by a highly experienced and well-trained medical staff. Subsequently, the interpretation of those findings could be easily influenced by subjective factors and therefore can be prone to diagnostic errors as evidenced in the present study. Breast MRI, is a diagnostic practice indicated in cases of high breast density and at the same time, a high-cost examination for healthcare systems. Furthermore, genetic testing that is used to diagnose hereditary breast cancer, represents a small proportion of breast cancers and at the same time it is a high-cost specialized test. Even the most widely used biopsy, Fine Needle Aspirate (FNA), in some cases involves risks and provides false negative results. This study presents a novel intelligent Clinical Decision Support System (CDSS), which uses data from common practice, non-invasive and low-cost diagnostic tests, together with medical health record, in order to provide clinicians with a viable, cost-effective and accurate diagnostic solution. After implementing several algorithms, Random Forest classifier showed the highest values of sensitivity 96,2 %, specificity 94,6%, PPV 96,2% and NPV 94,6%, being thus an effective algorithm in the development of our innovative CDSS model aiming to constitute a very useful tool in clinical practice for breast cancer early diagnosis.