Earticle

초록

영어

Accurate yet accessible prediction of Alzheimer’s disease (AD) remains difficult because advanced imaging and cerebrospinal fluid assays are expensive and invasive. This study explores whether two routinely collected clinical measures— patient age and Mini-Mental State Examination (MMSE) score—can form a reliable, interpretable baseline for AD risk modeling. A subject-level cohort of 4,651 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was analyzed using a logistic-regression framework with z-score scaling and balanced class weighting. Five-fold stratified crossvalidation produced stable performance (AUC = 0.929 ± 0.007, precision = 0.699 ± 0.007, recall = 0.844 ± 0.016, F1 = 0.765 ± 0.004, accuracy = 0.86 ± 0.004). Despite using only two features, the model approached the performance of several reported multimodal systems in discriminative power while remaining transparent and reproducible. This minimal benchmark establishes a reference point for future multimodal extensions and demonstrates that cognitively based screening can achieve clinically meaningful accuracy in resource-limited settings.

목차

Abstract
I. INTRODUCTION
II. DATASET AND METHODOLOGY
III. EXPERIMENTAL RESULTS
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Faizaan Fazal Khan [ Information and Communication Engineering Chosun University Gwangju, South Korea ]
• Vyshnavi Ramineni [ Information and Communication Engineering Chosun University Gwangju, South Korea ]
• Jae-Young Pyun [ Information and Communication Engineering Chosun University Gwangju, South Korea ]
• Goo-Rak Kwon [ Information and Communication Engineering Chosun University Gwangju, South Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004