AI-POWERED EARLY DETECTION OF ALZHEIMER’S DISEASE IN ELDERLY POPULATION IN LAHORE: A CROSS-SECTIONAL STUDY
DOI:
https://doi.org/10.71000/nra99x57Keywords:
Aged, Alzheimer Disease, Artificial Intelligence, Cognitive Dysfunction, Early Diagnosis, Machine Learning, Natural Language ProcessingAbstract
Background: Alzheimer’s disease (AD) is the most prevalent form of dementia, posing a growing burden in aging populations, especially in low- and middle-income countries. Early detection remains a significant challenge due to limited access to diagnostic resources and specialist care. Artificial intelligence (AI), particularly speech-based analysis, offers promise for non-invasive, cost-effective screening in such contexts.
Objective: To investigate the role of artificial intelligence in the early detection of Alzheimer’s symptoms using speech-based features among elderly residents of Lahore, Pakistan.
Methods: A cross-sectional study was conducted over eight months, involving 388 elderly participants aged 60 and above. Standardized cognitive assessments (MMSE and MoCA) were administered alongside AI-driven linguistic analysis using supervised machine learning algorithms. Speech samples from guided verbal tasks were processed using natural language processing techniques, and features such as lexical diversity, fluency, and syntactic complexity were extracted. Models including Random Forest, SVM, and Logistic Regression were evaluated for diagnostic performance. Statistical tests included t-tests, chi-square tests, and multivariate logistic regression.
Results: Among participants, 34.0% exhibited mild cognitive impairment and 18.6% showed signs of early Alzheimer’s. The Random Forest classifier achieved the highest diagnostic accuracy (88.4%), with sensitivity and specificity above 85%. AI-linguistic features significantly predicted early cognitive impairment (OR = 1.35; 95% CI: 1.22–1.49; p < 0.001), even after adjusting for age, education, and family history.
Conclusion: AI-based speech analysis demonstrates strong potential as a screening tool for early Alzheimer’s detection in urban elderly populations. Its application in low-resource settings may enhance timely diagnosis and intervention planning.
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Copyright (c) 2025 Muhammad Adnan Aslam, Muhammad Javaid Mushtaq , Asmarah Nadeem , Rana Muhammad Farooq Sattar , Muhammad Suhail , Rizwan Asghar (Author)
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