Discovering Diabetes Complications: an Ontology Based Model

Abstract

Background:Diabetes is a serious disease that spread in the world dramatically. The diabetes patient has an average of risk to experience complications. Take advantage of recorded information to build ontology as information technology solution will help to predict patients who have average of risk level with certain complication. It is helpful to search and present patient’s history regarding different risk factors. Discovering diabetes complications could be useful to prevent or delay the complications.Method:We designed ontology based model, using adult diabetes patients’ data, to discover the rules of diabetes with its complications in disease to disease relationship.Result:Various rules between different risk factors of diabetes Patients and certain complications generated. Furthermore, new complications (diseases) might be discovered as new finding of this study, discovering diabetes complications could be useful to prevent or delay the complications.Conclusion:The system can identify the patients who are suffering from certain risk factors such as high body mass index (obesity) and starting controlling and maintaining plan.

DMTO: a realistic ontology for standard diabetes mellitus treatment

“…However, these ontologies cannot be extended to a global diabetes treatment ontology for many reasons. First, they handle very limited corners of the problem, such as food [ 63 ], complications [ 64 ], follow-up [ 35 ], drugs [ 14 , 65 ], education [ 66 ], diet [ 67 ], physical activity [ 68 ], and questioners [ 33 ]. Secondly, they have not been implemented in a modular way, so the semantics of their terms, relations, and axioms are not clear.…”

Baduanjin Exercise for Type 2 Diabetes Mellitus: A Systematic Review and Meta‐Analysis of Randomized Controlled Trials

“…Approximately 87% to 91% of people with diabetes mellitus are estimated to have T2DM in high-income countries [ 3 – 6 ]. The diabetic is at high risk for experiencing complications such as cardiovascular disease, kidney disease, and diabetic neuropathy [ 7 ]. The international economy is suffering a great loss due to the increasing of the complications of diabetes mellitus, such as morbidity, disability, and mortality, especially in the developing countries [ 8 ].…”

An Ontology Driven System to Predict Diabetes With Machine Learning Techniques

“…As diabetic is dominating there is a huge quantity of data that produce from medical history of diabetes patients, there is massive interest in extract useful information and discover the hidden patterns. An information tool provides the ontology elucidation in healthcare domain [4]. The advantage of large amount of data that generate by such diseases, reclaim the others acquaintance to classify current needs in order to improve the upshot in the prospect iterations [5].…”

“…The idea of decision is based on if-then rule, using a tree structure beginning with a single node, then node getting expanded as a leaf [3,8]. Random forest is a combination of a greater number of Decision tree and it is a multi-functional algorithm, after training the samples unseen samples can be found from the trained samples [4]. Neural network has two types feed-forward and back-propagated neural network.…”