Optical coherence tomography (OCT) is a widely used imaging modality for diagnosing and monitoring macular diseases, including diabetic macular edema (DME) and choroidal neovascularization (CNV), both of which can cause severe visual impairment. Clinicians rely on various OCT biomarkers to identify these conditions. An algorithm was developed in Python to extract biomarker-associated features from OCT images and applied to a pre-labeled dataset containing normal, DME, and CNV images. Distribution analysis confirmed that the extracted features aligned with the existing literature. Using these features, LightGBM classified the OCT images, achieving 91% accuracy and 98% area under the receiver operating characteristic curve. Based on these promising results, this algorithm could contribute to the development of more advanced feature extraction methodologies for the diagnosis of macular diseases using traditional machine learning approaches. Such algorithms could potentially be integrated into automated patient screening systems.