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Open Access

ARTICLE

A Multi-Input Neural Network for Early Detection of Neurological Disordersfrom Vocal and Sleep Data

Md. Shujan Shak1, Nabila Rahman2, Fuad Mahmud3, Ashim Chandra Das1, M. F. Mridha4, Md. Jakir Hossen5,*
1 Doctor of Computer Science Program, University of the Potomac, Virginia, VA, USA
2 Doctor of Information Technology Program, Trine University, Detroit, MI, USA
3 Information Assurance and Cybersecurity, Gannon University, Erie, PA, USA
4 Department of Computer Science, American International University-Bangladesh, Dhaka, Bangladesh
5 Center for Advanced Analytics (CAA), COE for Artificial Intelligence, Faculty of Engineering & Technology (FET), Multimedia University, Melaka, Malaysia
* Corresponding Author: Md. Jakir Hossen. Email: jakir.hossen@mmu.edu.my
(This article belongs to the Special Issue: Artificial Intelligence and Machine Learning in Healthcare Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.081102

Received 23 February 2026; Accepted 20 May 2026; Published online 09 September 2026

Abstract

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.

Keywords

Neurological disorders; deep learning; multimodal learning; Parkinson’s disease; clinical decision support

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