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…
Read the paper
Related studies
- A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
- Toxicity of nanomaterials
- The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies
- Halogen bonding (X‐bonding): A biological perspective
- Biofilm formation at the air–liquid interface by the Pseudomonas fluorescens SBW25 wrinkly spreader requires an acetylated form of cellulose
- Opioid Activation of Toll-Like Receptor 4 Contributes to Drug Reinforcement
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.