Preparation Datasets Based on Artificial Intelligence Models and Classification Algorithms
Аннотация
In this study, artificial intelligence models were selected for preparing and diagnosing a dataset of diabetes mellitus. The effectiveness of artificial intelligence algorithms in the early detection of diabetes was analyzed.To achieve good efficiency results, the presence of NaN values and categorical values in the dataset, as well as ensuring a balanced dataset, led to an improvement in classification results. For this purpose, data preprocessing, feature selection, and testing of various classification models were performed based on real medical data. Using genetic algorithms, the NaN values of the most diagnostically significant factors were filled in. Based on the prepared data, classification models with high accuracy were created. These models enable the automation of medical diagnostic processes based on Logistic Regression, K-NN, Decision Tree, and Naive Bayes algorithms, as well as assist patients in early detection of disease risks. This research may contribute to improving the efficiency of the healthcare system through early detection of diabetes.