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Oral health prediction in patients with diabetes using artificial intelligence tools
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Farzad Firouzi jahantigh *    , Hoda Ghaeini hessarouieh , Zahra Ghorbani |
| Sistan and Baluchestan University, Zahedan , Firouzi@eng.usb.ac.ir |
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Abstract: (668 Views) |
Background and Aim: Diabetes may increase the incidence of tooth decay due to dry mouth and high blood sugar levels. Identifying the factors influencing oral health behaviours in diabetic patients is thus an essential step toward promoting oral and dental health. As a result, this study aimed to predict oral health in people with diabetes and compare them to healthy people.
Material and Methods: The available sampling method was used to conduct this study from 2021 to 2022. The study group consisted of 261 persons (men and women), 131 of whom were healthy and 130 of whom were unhealthy (diabetic), and information was gathered through a questionnaire, medical records, and an examination. These people looked at six variables: age, gender, decayed teeth, extracted teeth, filled teeth, and oral health index. Using the Spss Modeler program, two decision tree methods and a support vector machine and spss Modeler soft ware were used.
Results: The most important findings of decision tree analysis are 1- If the person's age is less than or equal to 37 years, then the person is 100% healthy. 2- If the age is over 37 years and the number of decayed teeth is less than the average of 7, and we do not have any extracted teeth, there is an 82% chance of diabetes. If the age is over 37 and the number of decayed teeth is less than the average of 7, and the number of extracted teeth is more than 1, then people under the age of 49 with an OHI index greater than 0.9 are 100% diabetic. Also, the total accuracy of the linear support vector machine is 70.73%, which indicates that decayed teeth with the least amount of weight have little effect on diabetes or health.
Conclusion: Decision tree algorithms and support vector machines could predict oral and dental health in diabetic patients.
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| Keywords: Health, Oral and dental, Diabetes, Decision Tree, Vector Machine, Artificial Intelligence |
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Full-Text [PDF 676 kb]
(387 Downloads)
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Type of Study: original article |
Subject:
Oral Medicine
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