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Introduction
Machine learning (ML) algorithms and statistical modeling offer a potential solution to offset the challenge of diagnosing early Alzheimer's disease (AD) by leveraging multiple data sources and combining information on neuropsychological, genetic, and biomarker indicators. Among others, statistical models are a promising tool to enhanc...
Citations
... In terms of the model's performance, its ability to distinguish between AD and non-AD is excellent. Previous studies have rarely focused only on the contribution of Tau and Aβ proteins to constructing AD diagnostic models, often incorporating some other biomarkers (Gaetani et al., 2021;Ficiarà et al., 2021;Franciotti et al., 2023;Khan et al., 2024). These studies have their own innovations and strengths, but inevitably have some shortcomings. ...
Background
Alzheimer's disease (AD) has a major negative impact on people's quality of life, life, and health. More research is needed to determine the relationship between age and the pathologic products associated with AD. Meanwhile, the construction of an early diagnostic model of AD, which is mainly characterized by pathological products, is very important for the diagnosis and treatment of AD.
Method
We collected clinical study data from September 2005 to August 2024 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Using correlation analysis method like cor function, we analyzed the pathology products (t-Tau, p-Tau, and Aβ proteins), age, gender, and Minimum Mental State Examination (MMSE) scores in the ADNI data. Next, we investigated the relationship between pathologic products and age in the AD and non-AD groups using linear regression. Ultimately, we used these features to build a diagnostic model for AD.
Results
A total of 1,255 individuals were included in the study (mean [SD] age, 73.27 [7.26] years; 691male [55.1%]; 564 female [44.9%]). The results of the correlation analysis showed that the correlations between pathologic products and age were, in descending order, Tau (Corr=0.75), p-Tau (Corr=0.71), and Aβ (Corr=0.54). In the AD group, t-Tau protein showed a tendency to decrease with age, but it was not statistically significant. p-Tau protein levels similarly decreased with age and its decrease was statistically significant. In contrast to Tau protein, in the AD group, Aβ levels increased progressively with age. In the non-AD group, the trend of pathologic product levels with age was consistently opposite to that of the AD group. We finally screened the optimal AD diagnostic model (AUC=0.959) based on the results of correlation analysis and by using the Xgboost algorithm and SVM algorithm.
Conclusion
In a novel finding, we observed that Tau protein and Aβ had opposite trends with age in both the AD and non-AD groups. The linear regression curves of the AD and non-AD groups had completely opposite trends. Through a machine learning approach, we constructed an AD diagnostic model with excellent performance based on the selected features.
This study employed bibliometric analysis using the Scopus database to evaluate Saudi disability research (SDR). From an initial dataset of 17,102 documents (0.54% of global output), the scope was refined to 13,246 data-driven publications for detailed examination. Trends, themes, and collaborations were analyzed using R packages and VOSviewer. Metrics such as citations, total link strength (TLS), and thematic mapping were used to identify key contributors, emerging topics, and international partnerships. Saudi authors demonstrated strong international collaboration, with 59.53% of publications involving co-authorships, particularly with the United States, Egypt, and India. Prolific contributors include Alkuraya, F.S. and leading institutions such as King Saud University. Key motor themes include “quality of life” and “Alzheimer’s disease,” while emerging themes such as “deep learning” and “molecular docking” reflect a shift toward advanced technologies. Machine learning is a trending topic applied in early diagnosis, drug discovery, and rehabilitation of conditions such as Alzheimer’s disease, autism, and epilepsy. These findings underscore the evolving priorities and global relevance of SDR.
