title: Design of a fault-tolerant decision-making system for biomedical applications.
abstract: This paper describes the design of a fault-tolerant classification system for medical applications. The design process follows the systems engineering methodology: in the agreement phase, we make the case for fault tolerance in diagnosis systems for biomedical applications. The argument extends the idea that machine diagnosis systems mimic the functionality of human decision-making, but in many cases they do not achieve the fault tolerance of the human brain. After making the case for fault tolerance, both requirements and specification for the fault-tolerant system are introduced before the implementation is discussed. The system is tested with fault and use cases to build up trust in the implemented system. This structured approach aided in the realisation of the fault-tolerant classification system. During the specification phase, we produced a formal model that enabled us to discuss what fault tolerance, reliability and safety mean for this particular classification system. Furthermore, such a formal basis for discussion is extremely useful during the initial stages of the design, because it helps to avoid big mistakes caused by a lack of overview later on in the project. During the implementation, we practiced component reuse by incorporating a reliable classification block, which was developed during a previous project, into the current design. Using a well-structured approach and practicing component reuse we follow best practice for both research and industry projects, which enabled us to realise the fault-tolerant classification system on time and within budget. This system can serve in a wide range of future health care systems.
authors.affiliation: School of Engineering, Ngee Ann Polytechnic Singapore, 599489, Singapore. fol2@np.edu.sg
authors.forename: Oliver
authors.initials: O
authors.lastname: Faust
authors.forename: U Rajendra
authors.initials: UR
authors.lastname: Acharya
authors.forename: Bernhard H C
authors.initials: BH
authors.lastname: Sputh
authors.forename: Toshiyo
authors.initials: T
authors.lastname: Tamura
mesh_terms: D003661:Decision Support Techniques; D016571:Neural Networks, Computer