Predicting metformin efficacy in improving insulin sensitivity among women with polycystic ovary syndrome and insulin resistance: a machine learning study

Abstract

ObjectiveMetformin is clinically effective in treating polycystic ovary syndrome (PCOS) with insulin resistance (IR), while its efficacy varies among individuals. This study aims to develop a machine learning model to predict the efficacy of metformin in improving insulin sensitivity among women with PCOS and IR. MethodsThis is a retrospective analysis of a multicenter, randomized controlled trial involving 114 women diagnosed with PCOS and IR. All women received metformin treatment for 4 months. We incorporated 27 baseline clinical variables of the women into the construction of our machine learning model. We firstly compared four commonly used feature selection methods to screen valuable clinical variables. Then we used the valuable variables as inputs to evaluate the performance of five machine learning models, including k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (Xgboost), in predicting the efficacy of metformin. ResultsAmong the five machine learning models, SVM performed the best with an area under the receiver operating characteristic curve (AUC) of 0.781 (95% confidence interval [CI]: 0.772-0.791). The key predictive variables identified were homeostasis model assessment of insulin resistance (HOMA-IR), body mass index (BMI), and low-density lipoprotein cholesterol (LDL-C). ConclusionThe developed machine learning model could be applied to to predict the efficacy of metformin in improving insulin sensitivity among women with PCOS and IR. The result could help doctors evaluate the efficacy of metformin in advance, optimize treatment plans, and thereby enhance overall clinical outcomes.