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Diagnosing diabetes using Catboost and bayesian methods
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Zahra Ahmadian *   , Farzad Eskandari |
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Abstract: (896 Views) |
| Today, the diagnosis of diseases using artificial intelligence and machine learning algorithms are of great importance, because by using the data available in the study field of the desired disease, useful information and results can be obtained that reduce the occurrence of many deaths. Among these diseases, we can mention the diagnosis of diabetes, which has spread today due to the growth of urban life and the decrease in people's activity. So, it is very important to know whether a person is suffering from diabetes or not. In this article, the data set related to the information of people who have done the diabetes diagnosis test is used, this information is related to 520 people. People are classified into two groups based on whether their diabetes test result is positive or not, and Bayesian classification methods such as Bayesian Support Vector Machine, Naive Bayes, CNK and CatBoost ensemble classification method have been used to conclude which of these The methods can have a better ability to analyze the data and also to compare these methods use accuracy, precision, F1-score, recall, ROC diagram. |
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| Keywords: classification, bayesian classification, ensemble classification |
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Full-Text [PDF 513 kb]
(348 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2023/04/27 | Accepted: 2024/02/6 | Published: 2024/03/15
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