AI Summary & Key Takeaways
AI InsightBesides, unlike similar studies examining the same dataset, the significance values of the factors associated with the models created were estimated.
📌 3 Key Takeaways
Abstract
Objective: In this study, it is aimed to classify type 2 Diabetes Mellitus (DM), compare the estimates of the Artificial Neural Network models and determine the factors related to the disease by applying Multilayer Perceptron (MLP) and Radial Based Function (RBF) methods on the open-access dataset. Material and Methods: In this study, the data set named “Pima Indians Diabetes Database” was obtained from https://www.kaggle.com/uciml/pima-indians-diabetes-database. The dataset contains 768 records with 268 (34.9%) type 2 diabetes patients and 500 (65.1%) people without diabetes, which have 9 variables (8 inputs and 1 outcome). MLP and RBF methods, which are artificial neural network models, were used to classify type 2 DM. Factors associated with type 2 DM were estimated by using artificial neural network models. Results: The performance values obtained with MLP from the applied models were accuracy 78.1%, specificity 81.2%, AUC 0.848, sensitivity 71%, positive predictive value 61.7%, negative predictive value 86.8% and F-score 66%. In relation to RBF model, the performance metrics were accuracy obtained 76.8%, specificity 82.1%, AUC 0.813, sensitivity 66.0%, positive predictive value 64.6%, negative predictive value 83% and F-score 65.3%, respectively. When the effects of the variables in the data set examined in this study on Type 2 DM are analyzed; The three most important variables for the MLP model were obtained as Glucose, BMI, Pregnancies respectively. For RBF, it was obtained as Glucose, Skin Thickness, and Insulin. Conclusion: The findings obtained from this study showed that the models used gave successful predictions for Type 2 DM classification. Besides, unlike similar studies examining the same dataset, the significance values of the factors associated with the models created were estimated.