Analytic Hierarchy Process Model for the Diagnosis of Typhoid Fever

Abstract

Typhoid fever is a global health problem, which seems neglected, but is responsible for significant levels of morbidity in many regions of the world, with about 12 million cases annually, and about 600,000 fatalities. Diagnosis of typhoid poses a great deal of challenge because its clinical presentation is confused with those of many other febrile infections such as malaria, yellow fever, etc. In addition, most developing countries do not have adequate bacteriology laboratories for further investigations. Decision support systems have been known to increase the efficiency and effectiveness of the diagnosis process, in addition to improving access; however, most existing decision support models for diagnosis of diseases have largely focused on 'non-tropical' conditions. An effective decision support model for diagnosis of tropical diseases can only be developed though the engineering of experiential knowledge of physicians who are experts in the management of such conditions. In this study, we mined experiential knowledge of twenty-five tropical disease specialist physicians to develop a decision support system based on the Analytic Hierarchy Process (AHP). The resulting model was tested based on 2044 patient data. Our model successfully determined the occurrence (or otherwise) of typhoid fever in 78.91% of the cases, demonstrating the utility of AHP in the diagnosis of typhoid fever.

Multi-criteria decision analysis method for differential diagnosis of tropical febrile diseases

“…Samuel et al 86 used Fuzzy AHP to predict heart failure obtaining an average accuracy of 91.10% while our study achieves highest accuracy of 96.9% in the prediction of LRTI. Furthermore, compared to Uzoka et al 58 where AHP method was used to predict typhoid obtaining an accuracy of 78.91%, our study achieves a prediction accuracy of 85.4% for the same febrile disease. The AHP engine for tuberculosis diagnosis in Fergus and Stephen 14 obtained an accuracy of 82% while the present study achieves an accuracy of 94.1% indicating significant improvements in providing sustainable solution as an information system for febrile disease diagnosis and treatment.

Figure 4.Accuracy: Physician vs. Model and Physician vs. Model+ diagnosis.

Figure 5.F1 score: Physician vs. Model and Physician vs. Model+ diagnosis.

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“…16,35,72 Some only develop singledisease models, identifying significant symptoms for each disease. 14,58,[73][74][75][76] While previous studies integrate physician judgment, 14,73,77 none has examined up to 11 diseases or compared DSS performance with confirmed diagnoses. This study, akin to Uzoka et al 16 and Osamor et al, 71 presents a user-friendly interface for field health workers (FHWs) and outlines earlier research on DSS development methods.…”

Prediagnosis of Disease Based on Symptoms by Generalized Dual Hesitant Hexagonal Fuzzy Multi-Criteria Decision-Making Techniques

“…A bacterial infection [28,41,43] called typhoid fever can spread throughout the body and harm numerous organs. It can lead to significant problems and even be fatal without early treatment.…”