System to assist in the Diagnosis of Diabetes using Ontology and Machine Learning
Traore Issa1, Oumtanaga Souleymane2, Claude Lishou3
1Traoré Issa*, his Institut of Mathematics research (IMAR), Felix Houphouet-Boigny University, Abidjan, Côte d’Ivoire.
2Oumtanaga Souleymane, Laboratory for Informatics and Telecommunications Research (LARIT), INPHB, 08 BP 475 Abidjan 08 (225), Cote d’Ivoire.
3Claude Lishou, Génie Électrique, Cheikh Anta Diop University, Dakar | UCAD · ESP, Dakar, Senegal.
Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9971-9975 | Volume-8 Issue-4, November 2019. | Retrieval Number: D4421118419/2019©BEIESP | DOI: 10.35940/ijrte.D4421.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Diabetes mellitus has become a public health problem in both developed and developing countries. If it is not treated early, diabetes-related complications in many vital organs of the body can become fatal. Its early detection is very important for early treatment that can prevent the disease from progressing to such complications. This article focuses on designing a system to assist in the diagnosis of diabetes disease based on medical ontology and automatic learning. The proposed method uses automatic learning algorithms as a classifier for the diagnosis of diabetes based on a medical data set. The ontology suggests a pre-processing of a coherent, consistent, interoperable and shareable knowledge basis of data and the machine learning method focuses on classification based on symptoms and medical tests. Based on the experimental results, DDAS not only offers better performance in predicting and diagnosing diabetes in individuals, but also has better accuracy in recommending useful treatment to patients.
Keywords: Ontology Machine Learning, Decision tree, diabete, Classification, Clinical decision support system.
Scope of the Article: Machine Learning.