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Among all of the skin diseases, Erythematous-Squamous Disease (ESD) is considered as the most complex one. It comprises of six types namely pityriasisrubra, lichen planus, chronic dermatitis, psoriasis, seborrheic dermatitis and pityriasisrosea. The primary reasons for inconsistent diagnosis are the common morphological features; this also makes the diagnosis stringent. ESD diagnosis is tremendously challenging, as it is not only based on the inculcated visible symptoms but also the physician’s expertise. A major factor for the evolution of Clinical Decision Support System (CDSS) is to integrate dermatology and medical software as it is an essential aspect of speciality-specific ontology.  Ontology aids in defining the semantics of the data and knowledge in a more formal way. It also helps in encoding the domain knowledge naturally for data mining purposes.  For modelling high-quality, linked and coherent data, ontology plays a vital role. Ontology’s role in the health care industry helps people to analyse the nature of the diseases and helps to treat them. To get high accuracy diagnosis and reduce error rate a Neural Network (NN) is used.For modelling, NN is considered as the most powerful one for which relationship between data is unavailable and for the data that are imprecise and noisy. Fuzzy logic gives flexibility for reasoning, which makes it probable to consider the inaccuracies and uncertainties. A fuzzy rule explicitly says that both the premise and the consequent are true to the same degree of the membership function. The hybrid Fuzzy Neural Network (FNN) obtained 99.4% of accuracy compared with existing work. Semantic Web Rule Language (SWRL) that merges Web Ontology Language (OWL) ontology’s to rule-based applications is achieved with the help of SWRL. Then the Query Language (SQWRL) can easily be used by SWRL to obtain the relational structure for the ONTOSkDS with DROOL inference engine.

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Hema D, & Dr.VasanthaKalyani David. (2021). ONTOSkDS: Clinical decision support system for skin diseases using ontology and hybrid FNN. International Journal of Intellectual Advancements and Research in Engineering Computations, 8(3), 677–687. Retrieved from