HML-PCD: A Hybrid machine learning technique for Early prediction and classification of celiac disease
Keywords:
Celiac disease (CD), data pre-processing, feature extraction, feature selection, CD prediction/classificationAbstract
This research proposes a hybrid machine learning approach for early celiac disease (CD) prediction. The method incorporates an enhanced cuttlefish optimization (ECO) algorithm for data pre-processing, removing unwanted artefacts. It employs an improved whale optimization algorithm (IWO) to extract multi-class features like texture and non-linear feature vectors for efficient disease detection. Subsequently, a modified crow search (MCS) algorithm optimizes feature selection, addressing dimensionality issues. Finally, a hybrid probabilistic deep neural network (PDNN) is introduced for CD prediction/classification, enhancing detection accuracy. The proposed approach demonstrates higher accuracy, precision, recall, and F-measure compared to current best practices.
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