Wide and deep learning based approaches for classification of Alzheimer’s disease using genome-wide association studies

Abstract

The increasing incidence of Alzheimer’s disease (AD) has been leading towards a significant growth in socioeconomic challenges. A reliable prediction of AD might be useful to mitigate or at-least slow down its progression for which, identification of the factors affecting the AD and its accurate diagnoses, are vital. In this study, we use Genome-Wide Association Studies (GWAS) dataset which comprises significant genetic markers of complex diseases. The original dataset contains large number of attributes (620901) for which we propose a hybrid feature selection approach based on association test, principal component analysis, and the Boruta algorithm, to identify the most promising predictors of AD. The selected features are then forwarded to a wide and deep neural network models to classify the AD cases and healthy controls. The experimental outcomes indicate that our approach outperformed the existing methods when evaluated on standard dataset, producing an accuracy and f1-score of 99%. The outcomes from this study are impactful particularly, the identified features comprising AD-associated genes and a reliable classification model that might be useful for other chronic diseases.

Predicting early Alzheimer’s with blood biomarkers and clinical features

“…For performance purposes, the genotyping data utilized in this study comprises the top 121 markers reported in a previous 2023 study 61 . Exact feature list can be found in Supplementary Fig.…”

“…Lastly, “bed” files provide machine codes that are unintelligible to humans. These codes are composed of 8-bit codes that map the data between fam and bim files and represent the genotype codes 61 .…”

“…Before the selection process, there were a total of 45 clinical features, 2,702,858 SNPs, and 19,403 gene expression features available. From the SNPs and gene expression features, 121 top features were chosen based on a well-established and previously published study 61 enhancing the robustness of our genetic data. The number of features selected by each method varies as it is an optimized hyperarameter.…”

Machine Learning to Advance Human Genome-Wide Association Studies

“…Recent research in Alzheimer's disease [69] used a hybrid feature selection approach based on association test, principal component analysis and the Boruta algorithm, to identify the most promising predictors. The selected features are then forwarded to wide and deep neural network models to classify the Alzheimer's disease cases and healthy controls.…”

“…The best-known longitudinal population-based biobanks include the UK Biobank [98], the China Kadoorie Biobank [99], the Estonian Biobank [100], and the Lifelines Biobank [101]. The use of this data through the implementation of AI methods has allowed high-throughput analysis and has led to new discoveries in the medical field [69] and shown to improve prediction in comparison to an unimodal approach [94].…”