Developing a Machine Learning Model to Detect the Early Stage of Alzheimer's Disease in Individuals with Mild Cognitive Impairment Utilizing Brain Imaging

Phuphiphat Jaikaew; Jatuphol Pholtaisong; Punnut Chirdchuvutikun; Chanodom  Lisantia

doi:10.53848/ssstj.v13i1.1122

Authors

Phuphiphat Jaikaew Thammasat University Rangsit Center
Jatuphol Pholtaisong Thammasat University Rangsit Center
Punnut Chirdchuvutikun Suankularb Wittayalai Rangsit School
Chanodom Lisantia Suankularb Wittayalai Rangsit School

DOI:

https://doi.org/10.53848/ssstj.v13i1.1122

Keywords:

Machine learning, Alzheimer’s disease, Mild cognitive impairment, Neuroimaging

Abstract

Alzheimer’s disease (AD), a form of dementia, significantly impairs an individual's ability to perform daily activities. The prevalence of AD patients in Thailand will increase to 1.1 million by 2030, marking AD as a critical concern in the nation's aging society. Mild Cognitive Impairment (MCI), often seen as a normal part of aging, has the potential to progress into AD. Early intervention in MCI cases could help delay the onset of AD. Our study focuses on creating a classification model leveraging neuroimaging data to distinguish between AD, MCI, MCI that has transitioned to AD (MCI_AD), and cognitively normal (CN) individuals. We utilized MRI brain images from the ADNI database, employing the FreeSurfer program to transform neuroimages into numerical data arrays for feature extraction. The study further included data preprocessing with standardization and feature selection by the scikit-learn library. Upon constructing five supervised machine learning models, we identified the one with the best performance through comparative analysis. The model’s effectiveness was evaluated using ROC curves, with the Support Vector Machine (SVM) model showing superior performance, achieving an area under the curve (AUC) of 0.764. Comparatively, Logistic Regression, NuSVC, Extra Trees Classifier, and MLP Classifier scored AUCs of 0.761, 0.758, 0.745, and 0.736, respectively. In summary, the SVM model demonstrated a notable ability to classify early stages of MCI and AD from other groups. Nevertheless, for further advancements in AD and MCI detection, exploring a wider array of datasets and feature extraction methodologies is imperative to improve the model’s performance.

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