Weighted Ontology and weighted tree similarity algorithm for diagnosing Diabetes Mellitus

Abstract

Application knowledge base for diabetes such as expert systems has been developed, but generally using conventional methods that have limitations in representing knowledge. Ontology supports the search of data / information by defining the concept of convergent intended by the user. This study using Diabetes Mellitus Classification based diabetes disease diagnosis from World Health Organization Geneva. This system receives input patient data from user. Then, system will build the patient ontology to represent patient knowledge. We are connecting Java applications to Protégé using OWL API. Then, system will calculate the weight of an ontology based on density. This system use JENA Inference Engine and working memory area for reasoning. The system would then do process similarity matching with Ontology Diabetes Mellitus using weighted tree similarity algorithm. Ontology has the highest similarity value will be the proposed diagnosis. Results of this study show that the representation in the form of OWL ontology using weighted ontology and weighted tree similarity algorithm can be used to represent knowledge about diabetes mellitus.

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

“…In the diabetes domain, ontology has been used in many CDSSs [13,23,[28][29][30]. For example, Chen et al [13] introduced an ontology for diabetes drugs and an ontology for patients' symptoms.…”

“…Sherimon et al [29] proposed a dynamic adaptive questionnaire ontology for gathering the diabetic patient's medical history. Hayuhardhika et al [30] developed an ontology for diabetes disease and used a weighted tree similarity algorithm for diagnosis. However, regarding diabetes diagnosis, none of these ontologies is designed for CBR, and few studies have used ontology in CBR [6,8].…”

Reusability ontology in business processes with similarity matching

“…In other words, ontology is a concept that systematically explains everything that exists. Ontology is the definition of basic understanding and vocabulary of a domain using the combination of terms and relations [8]. In the field of ontology Artificial Intelligence (AI) has two related terms.…”

Semantic representation and processing of hypoglycemic events derived from wearable sensor data

“…Other general health monitoring works include [23,15], although they do not consider streams of semantically enriched events, or the use of RDF stream processing engines. More specifically, for diabetes there have also been previous attempts that used ontologies for represent diabetic patient records and observations [25,14], although none of these approaches considers live data processing to detect patterns of interest.…”