Automated Neurocognitive Health Classification

DOI

10.2991/978-94-6239-789-7_10How to use a DOI?

Keywords

Neurocognitive disorders; machine learning; ensemble model; multimodal analysis; Alzheimer’s disease; speech analysis; facial video processing; cognitive health classification

Abstract

Neurocognitive disorders (NCDs) such as Alzheimer’s disease and dementia. These are increasing public health challenges that require timely and correct observation. Our study suggests a novel, multimodal ensemble-based machine learning (ML) framework that divides neurocognitive health into five levels. This system enhances disease recognition by using speech patterns, facial video analysis, and question-based responses. Old methods were based on a single data type and two categories (diseased or healthy). In contrast, our model combines the results of machine learning methods such as SVM, XGBoost, and Decision Tree using soft voting. This integration of diverse data types yields improved and more reliable results. Based on recent studies, the initial analysis proposes an accuracy of approximately 91 percent with a good F1 score. Still, it has not yet been tested on real-world data; this model demonstrates the potential of using machine learning for large-scale, understandable, and clinically useful cognitive health screening. Clinical trials and the inclusion of data from diverse ages and backgrounds are among the areas for future work.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rohit Kanauzia
AU  - Dinesh Singh Dhakar
AU  - Kamal Kumar Gola
AU  - Shweta Tiwari
AU  - Renu Bahuguana
AU  - Sabnoor Kaur
PY  - 2026
DA  - 2026/10/08
TI  - Automated Neurocognitive Health Classification
BT  - Proceedings of theInternational Conference on Innovation and Practices in Artificial Intelligence (ICIPAI 2026)
PB  - Atlantis Press
SP  - 128
EP  - 142
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-789-7_10
DO  - 10.2991/978-94-6239-789-7_10
ID  - Kanauzia2026
ER  -