## 1 Introduction  
Alzheimer's disease (AD) is the most common neurodegenerative disease that causes a demeting syndrome. It is clinically recognized by cognitive dysfunction, such as memory loss and behavioural changes that significantly impact functional ability [1]. AD is characterized pathologically by the abnormal accumulation of extracellular amyloid-\(\beta\) peptide (\(A\beta\)) plaques and intraneuronal neurofibrillary tangles (NFIs) composed of hyperphosphorylated tau protein in the brain [2, 3]. The abnormal accumulation of these proteins is thought to lead sequentially to neuroinflammation, neuronal cell death, synaptic dysfunction, and finally, cognitive impairment [2]. AD appears to be genetically dichotomous, with rare mutations in amyloid precursor protein (\(APP\)), presenilin 1 (\(PSEN\)), and presenilin 2 (\(PSEN\)) associated with early-onset familial AD, and apolipoprotein E4 (\(APOE4\)) polymorphism associated with an increased risk of late-onset AD [4]. The diagnostic accuracy for AD has increased with the use of new neuroimaging modalities, such as amyloid or tau positron emission tomography (PET) scans, and the evaluation of biomarkers in cerebrospinal fluid (CSF), obtained via lumbar puncture [5]. However, these procedures are not suitable for the screening of normal populations as they are prohibitively expensive or invasive [5]. Hence, attention has been drawn to the application of blood-based biomarkers, which is comparably more accessible and well-tolerated in regular clinical practice, to investigate and identify AD [5; 6]. MicroRNAs (miRNAs), which circulate in the peripheral blood system, may be potential biomarkers for AD. The emergence of next-generation sequencing (NGS) technology, such as RNA sequencing (RNA-seq), of small RNAs enables the reading of thousands or millions of miRNA molecules, lending an understanding of their roles in neurodegenerative diseases for investigation. miRNAs are small (approx. 18-25 nucleotides long), non-coding RNA molecules that regulate posttranscriptional gene expression by binding to the 3\({}^{\prime}\)-untranslated regions of messenger RNAs (mRNAs). The changes in expression of a miRNA can repress the translation of many mRNAs (gene silencing), influencing the amounts and functions of numerous proteins. A miRNA can target multiple mRNAs, including mRNAs that exert contradicting effects within the same molecular pathway [7]. Several miRNAs that regulate the synthesis of activity-mediated proteins, affecting the underlying processes of cognitive function and disease risk/progression in AD, have previously been identified. miRNAs are abundant and stable in human bodily fluids, including the blood and the CSF, as compared to mRNAs, making miRNAs easier to evaluate and study [8]. Studies investigating the possibility of miRNAs as biomarkers for AD suggested that the dysregulation of miRNAs in blood may be able to reflect the pathological process of neuronal impairment that occurs in AD [5; 6; 9]. Aberrant expressions of miRNAs have been identified in AD, such as miR-101, miR-20a, and miR-17, which appear to negatively regulate the expression of _APP_[10; 11]. Others, such as miR-22-3p and miR-340, were found to significantly alleviate _AB_ levels in AD [11], whereas miR-107 levels were found to be negatively correlated with _APOE4_[10; 11; 12; 13]. The suppression of miR-203 was also found to downregulate _APOE4_ and tau in mice [11]. The analysis of complex and highly heterogeneous AD expression data requires strong computational power to untangle the network of interactions between the miRNAs and to select the most likely candidates with the highest sensitivity and specificity in relation to AD [14]. The \"curse of dimensionality\", caused by the presence of large variables but a small sample size in a dataset, often poses the biggest challenge in the analysis of AD data [15]. The unbalanced ratio of the variables to the number of samples gives rise to the problem of overfitting and can increase false-positive results [14].