Publication:

Modeling the risk of age-related macular degeneration and its predictive comparisons in a population in South India

Background Objective of current study was to develop and cross-validate the prediction models for Age-related Macular Degeneration (AMD) by using Logistic Regression (LR) and Artificial Neural Networks (ANN).

Methods A population based cross-sectional epidemiologic study. The data (n=3723) were analyzed on participants aged ≥40 years in Andhra Pradesh, South India. Sub-population data from this sample was drawn by using random under sampling and random over sampling techniques to derive a risk score from the LR model. The models were compared for their predictive abilities by an Area under the Receiver Operating Characteristic Curve (AUROC).

Results The LR risk score was built with a score ranging from 0 to 60 for a sub-population dataset (n=213). A cut-off score of ≥30 had a sensitivity of 79% and a specificity of 69%. The predictive performance of ANN and LR was statistically equivalent (76% vs. 78%; P = 0.624). Both the models were stable and consistently obtained the same predictive accuracies in a 30-fold split-sample cross validation.

Conclusions The sensitivity analysis of the ANN model indicated the relative importance of prioritizing modifiable risk factors for AMD in order to base preventive interventions to reduce the impact of the modifiable factors on AMD.