Dhempster-Shafer Implementation in Overcoming Uncertainty in the Inference Engine for Diagnosing Oral Cavity Cancer

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The word uncertainty in an expert system is related to working with wrong data, wrong information, handling identical situations, the reliability of results, etc. Sources of uncertainty can come from unreliable information. This is usually caused by unclear domain concepts or for inaccurate data. One method for overcoming uncertainty is Dhempster-Shafer's theory. Dempster-shafers come up with approaches to calculate probabilities to look for evidence based on trust functions. In general the Dempster-Shafer theory is written at an interval [Confidence, Reasonable]. Belief (Bel) is a measure of the strength of evidence in support of a series of propositions. In this study an expert system will be developed to diagnose oral cancer that can recognize oral cancer based on the symptoms felt by the user. The results showed the Dempster-shafer was able to overcome the uncertainties in the construction of the inference engine, this is because the accuracy of the test results showed an accuracy of 86.6% Dempster-shafer.