Browsing by Author "Messaoudi, Noureddine(Directeur de thèse)"

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    Application of medical informatics and data analysis methods for automatic medical diagnosis
    (Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2025) Hammachi, Radhouane; Messaoudi, Noureddine(Directeur de thèse)
    With the increased size and complexity of data, interest has rapidly emerged in adopting artificial intelligence (AI) and deep learning (DL) to create data-driven models for automating medical diagnosis, and for neuromuscular disorders (NMDs) in particular. Therefore, this thesis aims to address the gaps in this context. To provide clinicians with a more objective and accurate methods for assessing muscle fatigue, a convolutional neural network (CNN)-based DL model was proposed to classify simulated surface electromyography (EMG) signals into different maximum voluntary contraction levels, achieving and accuracy of 88.88%. To ensure transparency and clinicians trust, the interpretability of Multi-Layer Perceptron (MLP) and Residual Neural Network (ResNet)-based DL models that achieved 95.67% and 98.37% testing accuracies, respectively, for myopathy diagnosis, was investigated. Shapley additive explanation (SHAP) for feature-based interpretation, and Gradient-weighted class activation mapping (Grad-CAM) for visual interpretation of raw signals, were employed, providing clear insights into the decision-making process. Furthermore, given the recent emergence and proved ability of quantum machine learning to handle high-dimensional data and solve complex tasks, a study was introduced to explore its potential in myopathy diagnosis. Quantum support vector machines (QSVMs) with variational quantum circuit-based kernels were proposed, and their performance was compared with classical methods. A hybrid QSVM model trained on deep features demonstrated promising classification ability, with training and testing accuracies of 96.7% and 85.1%, respectively. The results obtained in our research shed new light on the application of medical informatics in the field of healthcare and the EMG-based NMDs diagnosis in particular, indicating promising potential for future adoption of automated medical decision-making