Advancing biomedical data analytics using explainable neural network-based learning model for progressive neurodegenerative disorder diagnosis

Abstract Huntington’s disease (HD) is an inherited neurological disease caused by variations in the huntingtin (HTT) gene, which leads to neuronal degeneration. Conventionally, HD is affiliated with the gathering and misfolding of mutant HTT arising from an increased number of CAG triplets. Artificial Intelligence has emerged as an important tool in healthcare, supporting the monitoring, detection, and management of HD. Machine learning and deep learning methods are widely used for automated HD identification using neuroimaging, genetic, and clinical data. However, most DL models behave like a black box, making it difficult to interpret decision-making from clinical data, which reduces trust in medical applications. Therefore, this study presents an Explainable Neural Network-Driven Learning Model for Neurodegenerative Disorder Diagnosis (XNNLM-NDD). The primary objective of the proposed model is to examine clinical attributes and identify disease patterns efficiently for precise diagnosis. The model performs feature selection using a hybrid combination of minimum redundancy maximum relevance and ReliefF methods to select the most informative and non-redundant features from the dataset. For classification, the proposed approach employs a feature tokenizer-transformer model, which can capture complex feature interactions and improve classification accuracy on structured medical data. Furthermore, the model is optimized using the Cycle-Norm-Adam algorithm. For ensuring model transparency and interpretability, SHAP-based explainable artificial intelligence method is used to highlight the contribution of each feature towards the final prediction. The experimental evaluation is carried out on the Huntington Disease Dataset sourced from Kaggle. The results show that the proposed XNNLM-NDD approach accomplishes improved performance with an accuracy of 96.50% compared to existing techniques, indicating its efficiency in progressive neurodegenerative disorder diagnosis.