Using Machine Learning and Artificial Intelligence to Predict Diabetes Mellitus Among Women Population

Abstract

Introduction: Diabetes Mellitus (DM) is a chronic health condition (long-lasting) due to inadequate control of blood levels of glucose. This study presents a prediction of type 2 diabetes mellitus among women using various Machine Learning (ML) algorithms deployed to predict the diabetic condition. A University of California Irvine (UCI) diabetes mellitus dataset posted on Kaggle was used for analysis Methods: The dataset included eight risk factors for type 2 diabetes mellitus prediction, including age, systolic blood pressure, glucose, body mass index (BMI), insulin, skin thickness, diabetic pedigree function, and pregnancy. R language was used for the data visualization, while the algorithms considered for the study were logistic regression, Support Vector Machines (SVM), Decision Trees, and Extreme Gradient Boost (XGB). The performance analysis of these algorithms on various classification metrics was also presented, considering that the AUC-ROC score is the best for Extreme Gradient Boost (XGB) with 85%, followed by SVM and Decision Trees (DT). Results: The Logistic Regression (LR) demonstrated low performance, but the decision trees and XGB showed promising performance against all the classification metrics. Moreover, SVM offers a lower support value, so it cannot be considered a good classifier. The model showed that the most significant predictors of type 2 diabetes mellitus were glucose levels and body mass index, whereas age, skin thickness, systolic blood pressure, insulin, pregnancy, and pedigree function were less significant. This type of real-time analysis has proven that the symptoms of type 2 diabetes mellitus in women fall entirely different compared to men, which highlights the importance of glucose levels and body mass index in women. Conclusion: The prediction of type 2 diabetes mellitus helps public health professionals to suggest proper food intake and adjust lifestyle activities with good fitness management in women to make glucose levels controlled. Therefore, the healthcare systems should give special attention to diabetic conditions in women. This work attempts to predict the occurrence of type 2 diabetes mellitus among women from their various behavioral and biological conditions.