Design and Development of an Intelligent Clinical Decision Support System Applied to the Evaluation of Breast Cancer Risk

Abstract

Breast cancer is currently one of the main causes of death and tumoral diseases in women. Even if early diagnosis processes have evolved in the last years thanks to the popularization of mammogram tests, nowadays, it is still a challenge to have available reliable diagnosis systems that are exempt of variability in their interpretation. To this end, in this work, the design and development of an intelligent clinical decision support system to be used in the preventive diagnosis of breast cancer is presented, aiming both to improve the accuracy in the evaluation and to reduce its uncertainty. Through the integration of expert systems (based on Mamdani-type fuzzy-logic inference engines) deployed in cascade, exploratory factorial analysis, data augmentation approaches, and classification algorithms such as k-neighbors and bagged trees, the system is able to learn and to interpret the patient’s medical-healthcare data, generating an alert level associated to the danger she has of suffering from cancer. For the system’s initial performance tests, a software implementation of it has been built that was used in the diagnosis of a series of patients contained into a 130-cases database provided by the School of Medicine and Public Health of the University of Wisconsin-Madison, which has been also used to create the knowledge base. The obtained results, characterized as areas under the ROC curves of 0.95–0.97 and high success rates, highlight the huge diagnosis and preventive potential of the developed system, and they allow forecasting, even when a detailed and contrasted validation is still pending, its relevance and applicability within the clinical field.

A Genomic Information Management System for Maintaining Healthy Genomic States and Application of Genomic Big Data in Clinical Research

“…The clinical decision support system (CDSS) is a tool to improve the treatment effect and prognosis of patients by integrating established clinical knowledge and patient information, as shown in Figure 3 . At each stage, from diagnosis to follow-up, the CDSS helps clinicians make optimal decisions based on patient-derived information [ 47 , 48 ]. It is believed that utilizing the CDSS under appropriate regulation greatly optimizes patient treatment.…”

Fuzzy Support Vector Machine based Fall Detection Method for Traumatic Brain Injuries

“…The third part is the defuzzification process that converts the fuzzy sets into crisp values. Despite the availability of several defuzzification methods, the centroid technique remains the most popular among all of them [39,40].…”

“…In this paper, trapezoidal Membership Function (MF) was considered as this type is most frequently used, very flexible, and a small amount of data is needed to define it. The trapezoidal function guarantees the existence of a certainty interval in the fuzzification [40].…”

Design and Definition of a New Decision Support System Aimed to the Hierarchization of Patients Candidate to Be Admitted to Intensive Care Units

“…The developments and advances in artificial intelligence algorithms have allowed the reasoning and learning models to give support to decision support systems [ 29 ], thus allowing them to diversify and complement their usage. Starting from—either symbolic or statistical—inferential processes, approaches such as machine learning, deep learning or expert systems have the inherent capability to find probabilistic and/or logical relationships that allow for limiting the uncertainty and reducing the risk associated with decision making [ 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. However, the use of these techniques requires advanced learning models, together with complex representations of reasoning or learning that implies the need for the availability of a large starting dataset, or for experts in charge of processing such data and elaborate association rules.…”