2025· Proceedings of the 1st International Conference on Interdisciplinary Technology & Science Convergence (FusionX Global)· pp. 220-225· 0 citations· 14 references
TL;DR
This work can detect early signs of cognitive decline through handwriting patterns related to Alzheimer's Disease through machine learning (ML) Algorithms, which is simple, suitable for remote areas and cost-effective.
Abstract
: The neurodegenerative condition known as Alzheimer's disease is progressive and affects behavior, memory, and cognitive abilities. This disease is global health challenge led to various socio-economic challenges in world. People who receive an early diagnosis have better results and the disease's course is slowed down. Using machine learning (ML) Algorithms, we can detect early signs of cognitive decline through handwriting patterns related to Alzheimer’s Disease. For early Alzheimer's disease (AD) identification, the system gathers samples from raw Hand writing data from persons, extracts features such as stroke pressure, spacing, and curvature. To enhance prediction accuracy at an early stage, ML models like Random Forest, Decision Tree, Linear Regression, and XGBoost are used to early AD prediction from extracted samples. This advanced approach for early AD detection is simple, suitable for remote areas and cost-effective.
An interpretable deep learning framework for AD diagnosis using the DARWIN handwriting dataset is proposed, which integrates Recursive Feature Elimination with a Feedforward Neural Network (FNN) to identify the most discriminative handwriting features, reduce dimensionality, and enhance generalization.
M. Nasir, Uddalak Mitra· Vietnam Journal of Computer...· 0 citations
This review synthesizes recent advances in AI-based AD diagnosis, tracing the evolution from traditional ML to DL and LLMs and particular emphasis is placed on the emerging role of LLMs in extracting disease-related information from speech and clinical narratives, integrating heterogeneous biomedical data sources, and...
Xia-Yao Guo, Yan-Qi Sun, Yang Chen et al.· Biosensors· 0 citations
Online handwriting provides a non-invasive and low-cost behavioral biomarker for Alzheimer's disease (AD) detection, as it reflects both cognitive planning and fine motor control. Existing handwriting-based AD detection methods usually rely on global trajectory features or whole-sample representations, which can be str...
Chang-Qing Gong, Hua-Feng Qin, Mounîm A. El-Yacoubi· 0 citations
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that mostly affects older adults. It is distinguished by a slow decline in cognitive functions, including impaired reasoning, reduced problem-solving abilities, depression, and irritability. The disease results from the collection of abnormal protein...
Dhivyabharathi V. R., S. S., S. S.· Journal of Innovative Image...· 0 citations
Parkinson's Disease (PD) is a progressive brain disorder that affects human motor functions. Early Parkinson's Disease (PD) detection plays a major role in facilitating clinical treatment. Handwriting analysis has proven to be an efficient way to detect motor problems; nonetheless, traditional methods have shown poor f...
G. R, S. K.· Journal of Ubiquitous Comput...· 0 citations
Parkinson’s Disease (PD) is a neurodegenerative disorder that affects older individuals, especially in aging countries like Japan. A key symptom of PD is hand tremor, which impairs fine motor skills, including handwriting. Early detection is crucial for treatment and management. However, existing datasets often lack su...
Jungpil Shin, Abu Saleh Musa Miah, Akane Hashimoto et al.· Scientific Reports· 0 citations
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