Automatic Detection of Genetic Diseases in Pediatric Age Using Pupillometry

This paper introduces a comprehensive framework for automated disease detection using pupillometry data. Our approach establishes a robust pipeline that includes data preprocessing, feature extraction, and machine learning-based classification of patients based on their pupillary responses. We extract key features from both left and right pupil diameter measurements, such as maximum and minimum values, delta, channel height (CH), latency, and mean change velocity (MCV).To enhance classification accuracy, we train and evaluate multiple machine learning models, including Support Vector Machines (SVMs), an ensemble classifier, Extreme Learning Machines (ELM), Multi-Layer Perceptrons (MLPs), and Random Forests. Additionally, we propose a novel hybrid model that integrates the strengths of multiple algorithms, outperforming individual models in accuracy. Our experimental results highlight the effectiveness of this hybrid approach, demonstrating its potential for improving non-invasive and efficient disease diagnosis. This research contributes to advancements in clinical ophthalmology and neurology by leveraging pupillometry and machine learning for more precise and accessible diagnostic tools.