No abstract

In recent years, computational diagnostic tools and artificial intelligence techniques provide automated procedures for objective judgments by making use of quantitative measures and machine learning techniques. In this paper we propose a Support Vector Machines (SVMs) based classifier in comparison with Bayesian classifiers and Artificial Neural Networks for the prognosis and diagnosis of breast cancer disease. The paper provides the implementation details along with the corresponding results for all the assessed classifiers. Several comparative studies have been carried out concerning both the prognosis and diagnosis problem demonstrating the superiority of the proposed SVM algorithm in terms of sensitivity, specificity and accuracy.

In a modern technological environment where information systems are characterized by complexity, situations of non-effective operation should be anticipated. Often system failures are a result of insufficient planning or equipment malfunction, indicating that it is essential to develop techniques for predicting and addressing a system failure. Particularly for safety-critical applications such as the healthcare information systems, which are dealing with human health, risk analysis should be considered a necessity. This paper presents a new method for performing a risk analysis study of health information systems. Specifically, the CCTA Risk Analysis and Management Methodology (CRAMM) has been utilized for identifying and valuating the assets, threats, and vulnerabilities of the information system, followed by a graphical modeling of their interrelationships using Bayesian Networks. The proposed method exploits the results of the CRAMM-based risk analysis for developing a Bayesian Network model, which presents concisely all the interactions of the undesirable events for the system. Based on "what-if" studies of system operation, the Bayesian Network model identifies and prioritizes the most critical events. The proposed risk analysis framework has been applied to a vital signs monitoring information system for homecare telemedicine, namely the VITAL-Home System, developed and maintained for a private medical center (Medical Diagnosis and Treatment S.A.).

No abstract

PurposeThe paper presents an efficient methodology that was developed for the reliability prediction and the failure mode effects and criticality analysis (FMECA) of electronic devices using fuzzy logic.Design/methodology/approachThe reliability prediction is based on the general features and characteristics of the MIL‐HDBK‐217FN2 technical document and a derating plan for the system design is developed in order to maintain low components’ failure rates. These failure rates are used in the FMECA, which uses fuzzy sets to represent the respective parameters. A fuzzy failure mode risk index is introduced that gives priority to the criticality of the components for the system operation, while a knowledge base is developed to identify the rules governing the fuzzy inputs and output. The fuzzy inference module is Mamdani type and uses the min‐max implication‐aggregation.FindingsA typical power electronic device such as a switched mode power supply was analyzed and the appropriate reliability indices were estimated using the stress factors of the derating plan. The fuzzy failure mode risk indices were calculated and compared with the respective indices calculated by the conventional FMECA.Research limitations/implicationsFurther research efforts are needed for the application of fuzzy modeling techniques in the area of reliability assessment of electronic devices. These research efforts can be concentrated in certain applications that have practical value.Practical implicationsPractical applications can use a fuzzy FMECA modeling instead of the classical FMECA one, in order to obtain a more accurate analysis.Originality/valueFuzzy modeling of FMECA is described which can calculate fuzzy failure mode risk indices.