Abstract: Alzheimer's disease (AD), the most common type of dementia, is expected to affect 152 million people by 2050, emphasizing the importance of early diagnosis. This study uses the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, combining cognitive tests, biomarkers, demographic details, and genetic data to build predictive models. Using large language models (LLMs), specifically ChatGPT 3.5, we achieved high classification accuracy, with ROC AUC values of 0.98 for cognitively normal (CN) individuals, 0.99 for dementia, and 0.98 for mild cognitive impairment (MCI). These findings show that LLMs can handle complex data quickly and accurately. By focusing on numerical and text-based data instead of just imaging, this method provides a cost-effective and accessible option for diagnosing AD. Adding genetic information improves the predictions, reflecting the important role of genetics in AD risk. This study highlights the potential of combining different types of data with advanced machine learning and LSTM to improve early AD diagnosis. Future research should explore more ways to combine data and test different machine learning models to further enhance diagnostic tools.

Hassan Almalki, Alaa O. Khadidos and Nawaf Alhebaishi, “Enhancing Alzheimer's Detection: Leveraging ADNI Data and Large Language Models for High-Accuracy Diagnosis” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01511134

@article{Almalki2024,
title = {Enhancing Alzheimer's Detection: Leveraging ADNI Data and Large Language Models for High-Accuracy Diagnosis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01511134},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01511134},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Hassan Almalki and Alaa O. Khadidos and Nawaf Alhebaishi}
}