Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach

• Published in: Applied Computational Intelligence and Soft Computing Vol. 2022; pp. 1 - 11
• Main Authors: Jader, Rasool, Aminifar, Sadegh
• Format: Journal Article
• Language: English
• Published: New York Hindawi 22.10.2022; John Wiley & Sons, Inc; Wiley
• Subjects: Accuracy; Algorithms; Analysis; Blood sugar; Classification; Clustering; Data mining; Data reduction; Datasets; Decision trees; Diabetes in pregnancy; Diagnosis; Gestational diabetes; Hyperglycemia; Intelligent systems; Learning strategies; Machine learning; Prediction models; Pregnancy; Pregnant women; Support vector machines; Type 2 diabetes; Womens health; Kurdistan
• ISSN: 1687-9724; 1687-9732; 1687-9732
• DOI: 10.1155/2022/9749579

Gestational diabetes is a type of high blood sugar that develops during pregnancy. It can occur at any stage of pregnancy and cause problems for both the mother and the baby, during and after birth. The risks can be reduced if they are early detected and managed, especially in areas where only periodic tests of pregnant women are available. Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. This study proposes a combined prediction model to diagnose gestational diabetes. The dataset was obtained from the Kurdistan region laboratories, which collected information from pregnant women with and without diabetes. The suggested model uses the clustering KMeans technique for data reduction and the elbow method to find the optimal k value and the Mahalanobis distance method to find more related cluster to new samples, and the classification methods such as decision tree, random forest, SVM, KNN, logistic regression, and Naïve Bayes are used for prediction. The results showed that using a mix of KMeans clustering, elbow method, Mahalanobis distance, and ensemble technique significantly improves prediction accuracy.