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Temporal logic-based fuzzy decision support system for diagnosis of rheumatic fever and rheumatic heart disease

Temporal logic-based fuzzy decision support system for diagnosis of rheumatic fever and rheumatic heart disease

Pandey, Sanjib Raj (2016) Temporal logic-based fuzzy decision support system for diagnosis of rheumatic fever and rheumatic heart disease. PhD thesis, University of Greenwich.

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Abstract

This is a collaboration project between the Nepal Heart Foundation (NHF) and the University of Greenwich (UoG), United Kingdom (UK). Our mutual aim, agreed at the outset, has been to analyse, design and develop a cost effective Clinical Decision Support System (CDSS) for diagnosis and recognition of Acute Rheumatic Fever (ARF) and Rheumatic Heart Disease (RHD) at an early stage by developing/adopting UK’s and NHF’s treatment practices and procedures that would be appropriate for the Nepalese environment and lifestyle. The Application we developed was designed for use by community health workers and doctors in the rural areas of Nepal where laboratory facilities, expert services and technology are often deficient.

The research undertaken investigated three problems that previously had not been addressed in the Artificial Intelligence (AI) community. These are: 1) ARF in Nepal has created a lot of confusion in diagnosis and treatment, due to the lack of standard procedures; 2) the adoption of foreign guidelines is often not effective and does not suit the Nepali environment and lifestyle and 3) the value of combining (our proposed) Hybrid Approach (Knowledge-based System (KBS), Temporal Theory (TT) and Fuzzy Logic (FL)) to design and develop an application to diagnose ARF cases at an early stage in English and Nepali.

This research presents, validates and evaluates a proposed Hybrid Approach to diagnose ARF at three different stages: 1) Detected; 2) Suspected and 3) Not-detected and also to identify the severity level of detected ARF in the forms of Severe Case, Moderate Case or Mild Case. The Hybrid Approach is a combination of the KBS/Boolean Rule Model, Temporal Model and Fuzzy Model. The KBS/Boolean Rule Model has four components for design and implementation of KBS. These are: identifying the ARF stage in a case; Rule Pattern Matching; New Rule Formation and Rule Selection Mechanism. The Temporal Model has four components namely: Descriptive Explanation of ARF symptoms; Temporal Lookup-Table/Rules and Temporal Reasoning which produce a Temporal Template for demonstrating the relationship between the signs and ARF. The Fuzzification, Fuzzy Inferences and Defuzzification components are applied to design and implement a Fuzzy Model. The research undertaken divided the overall ARF diagnosis problem, in effect its requirements, into several sub-problems and each model of the Hybrid Approach addressed particular sub-problems for example, Identify the stage of the ARF component of the KBS/Boolean Rule Model used to solve the question of identifying the stage of ARF based on the symptoms presented. Each problem was therefore handled using a particular model’s components. This significantly helped to improve maintainability, reliability and the overall quality of our final ARF Diagnosis Application.

The developed ARF Diagnosis Application was experimentally tested and evaluated by NHF’s experts and users through applying NHF’s data sets consisting of 676 real patients’ records. The ARF Diagnosis Application was found to match 99% of the cases derived from NHF’s datasets. The overall ARF diagnostics performance and accuracy was 99.36%. Therefore, the experiments and evaluations of our ARF Diagnosis Application proved that it was logically and technically feasible to employ the Hybrid Approach for developing a new and practical ARF Diagnosis Application. The Application was ultimately developed and succeeded in embracing NHF’s requirements and guidelines thereby matching the Nepalese setting and making it suitable for use in Nepal having fully by met the exigencies of the Nepalese environment and lifestyle. Application of a new ARF diagnosis system (ours) proved that the Hybrid Approach, applied methods of diagnosis of ARF, medication and treatment plan, including help and supporting information which were identified and defined, were shown to be appropriate to support effectively community health workers and doctors who actively care for ARF and RHD cases in rural Nepal.

Item Type: Thesis (PhD)
Uncontrolled Keywords: temporal theory; fuzzy logic; acute rheumatic fever;
Subjects: Q Science > QA Mathematics
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Department of Mathematical Sciences
Last Modified: 20 Nov 2017 12:06
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/18088

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