Evolutionary AdaBoost ensemble: A machine learning framework for depression detection

Depression is a prevalent and debilitating mental health disorder that often goes undiagnosed due to the lack of accessible, objective screening tools. This paper introduces EVAdaBoost, an Evolutionary AdaBoost ensemble framework designed for automated depression detection from voice signals. The method leverages a diverse set of signal processing techniques—including Fourier, Wavelet, Walsh, Hilbert–Huang, and OpenSmile, as well as time–frequency transformations for convolutional neural networks (CNNs). Each feature set is used to train a specialised AdaBoost ensemble, with Broad Learning Systems (BLS) serving as efficient weak learners. A key innovation of EVAdaBoost is its use of a quantum-inspired evolutionary algorithm to optimise the feature subsets assigned to each AdaBoost model. Instead of using all extracted features, which may include noise, redundancy, and irrelevant data, EVAdaBoost evolves to select diverse and high-performing subsets of features for each AdaBoost base learner, automatically discarding non-informative features. This evolutionary selection enhances both classification accuracy and computational efficiency. Additionally, an evolutionary pruning algorithm is employed to find the optimal subset of AdaBoost algorithms that offer the best performance at reduced computational cost. Experiments across nine feature types and multiple benchmark classifiers show that EVAdaBoost consistently outperforms state-of-the-art methods in accuracy, sensitivity (TPR), specificity (TNR), and precision (PPV). The results underscore the potential of hybrid evolutionary ensemble learning for non-invasive, speech-based mental health screening.