Diagnosis of Diabetes Using Bayesian and Boosting Classifier
Abstract
In recent years, the diagnosis of diseases using artificial intelligence and machine learning algorithms has gained significant importance. Using data from relevant medical studies enables the extraction of valuable insights that can reduce the occurrence of numerous fatalities. One of the rapidly growing chronic diseases is diabetes, which has shown a growing prevalence due to urbanization and reduced physical activity. Hence, early detection of diabetes in individuals has immense significance. This paper utilizes a dataset comprising information from individuals who underwent diabetes diagnostic tests and employs classification techniques to determine whether their test results were positive or negative for diabetes. The novelty of this work lies in the comparative analysis of Bayesian classifiers and boosting methods, which have not been extensively explored in the literature. The utilized classification methods include Bayesian classifiers such as Bayesian Support Vector Machine, Bayesian k-nearest neighbor, Bayesian decision tree and boosting methods like Catboost, Adaboost, and XGboost. Performance evaluation metrics, including accuracy, precision, recall, F1-score, and ROC curve analysis, are employed to compare the efficacy of these methods in analyzing the data. The findings of this study will contribute to the advancement of accurate and efficient diabetes diagnosis using machine learning techniques, potentially helping in early intervention and management of the disease.
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