Comparison of Certainty Factor and Forward Chaining for Early Diagnosis of Cats Skin Diseases

Abstract

Cats are animals that are prone to skin diseases if not treated properly. It can happen because many bacteria and fungi attached to the skin of cats due to not treated properly. Skin diseases in cats have diverse causes as well as diverse types of consequences, and early diagnosis are diverse. Certainty Factor and Forward Chaining are multi attribute methods to infer a fact-based problem and hypotheses usually used in expert systems. Forward chaining itself is data-driven because the inference starts from the available information and is then inferred. Certainty Factor uses a definite or uncertain hypothesis method that uses metrics whereas forward chaining uses data driven from available information and after that comes the conclusion. With both methods designed to determine the early diagnosis of cat disease. Some of the diverse symptoms and conclusions, as well as hypotheses obtained from medical experts, are used to diagnose the onset of cat skin disease symptoms.