A novel data mining approach for early diagnosis of gestational diabetes mellitus (GDM) in pregnancy via machine learning methods and CNN

Purpose: The aim of this study was to investigate a novel data mining approach for early and effective diagnosis of Gestational Diabetes Mellitus (GDM). Methods: Gestational Diabetes Mellitus (GDM) data contains two classes (healthy and diabetic), 15 features and 3525 instances. In the first stage, the widely used and effective KNN and regression methods were employed for the filling of missing data. Then, the data source transformed into grayscale images as primary images and multiplexed images. Finally, both original data and transformed data are classified with KNN, SVM and CNN using k-fold cross validation technique. Performance metrics were compared to extract the best suitable system. Results: The original GDM source and the missing values replacement of GDM are classified with KNN and SVM methods. Also, primary images of this dataset and multiplexed images are classified with CNN 50%-50% and 70%-30% train-test respectively. The results of classification performance demonstrated that reaching up to 97.91% with CNN, recall of 97.61%, specificity of 97.61%, precision of 97.97% and F1-score of 97.79%. This result outperformed all previous studies conducted on the same dataset in the literature. Conclusions: This work is demonstrated a new approach that the best results of classification accuracy when compared with previous studies related to proposed methods to identify GDM disease. It can be clearly stated that applying a data mining method to impute missing values, followed by converting the dataset into images based on certain criteria and classifying with CNN, is the most effective approach for predicting GDM.