Application of Machine Learning Predictive Models for Early Detection of Glaucoma Using Real World Data

Early detection of glaucoma is critically important for the prevention of irreversible blindness. We developed a predictive analytic framework through temporal data carpentry and applications of a suite of machine learning and logistic regression methods for the early prediction of glaucoma using electronic health records (EHR) from over 650 hospitals and clinics across the USA. Four different machine-learning classification methods were applied using the whole dataset for predictive analysis. The accuracy, sensitivity, specificity, and f1 score were calculated using five-fold cross-validation to train and refine the models. The XGBoost, multi-layer perceptron (MLP), and random forest (RF) performed comparably well based on the area under the receiver operating characteristics curve (AUC) score of 0.81 for predicting glaucoma one year before the onset of the disease compared to the logistic regression (LR) score of 0.73. This study suggests that the ML methods can capture potential pre-glaucoma patients in advance before the occurrence of clinical symptoms from their history of EHR encounters, thus possibly leading to earlier intervention and preventive treatment.

Automated summary

Early detection of glaucoma is crucial for preventing irreversible blindness. A predictive analytic framework was developed using machine learning and logistic regression methods on electronic health records (EHR) from over 650 hospitals and clinics in the USA. The study found that the XGBoost, MLP, and RF methods performed well in predicting glaucoma one year before onset, with an AUC score of 0.81, compared to 0.73 for logistic regression. This suggests that machine learning methods can identify potential pre-glaucoma patients in advance, leading to early intervention and prevention.