TY  - EJOU
AU  - Shak, Md. Shujan 
AU  - Rahman, Nabila 
AU  - Mahmud, Fuad 
AU  - Das, Ashim Chandra 
AU  - Mridha, M. F. 
AU  - Hossen, Md. Jakir 

TI  - A Multi-Input Neural Network for Early Detection of Neurological Disordersfrom Vocal and Sleep Data
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - Early detection of neurological disorders is critical for effective treatment planning and improved quality of life. This study proposes a multi-input deep neural network that integrates vocal biomarkers and clinical sleep-related features to improve diagnostic accuracy. The model processes each modality through separate neural branches before combining high-level representations for final classification. We evaluate the approach using two public datasets: a Parkinson’s disease dataset containing 1195 voice samples and a sleep-disorder dataset with 80 patient records. Experimental results show that the proposed model outperforms classical machine learning baselines and single-modality deep learning models, achieving an accuracy of 92.5%, precision of 90.2%, recall of 94.1%, F1-score of 92.1%, and an AUC-ROC of 0.953. The architecture demonstrates consistent improvements over early and late fusion strategies, demonstrating the benefit of modality-specific representation learning. This work provides a scalable and non-invasive diagnostic framework with strong potential for clinical screening and monitoring.
KW  - Neurological disorders; deep learning; multimodal learning; Parkinson’s disease; clinical decision support

DO  - 10.32604/cmc.2026.081102