## 1 Introduction
Although some statistical methods have been reported to perform well with such data comprising smaller sample sizes and high biological replicates [16; 17], machine learning methods are deemed to be more reliable in solving data overfitting problems [18]. Feature-selection methods and cross-validation steps carried out during the analysis reportedly perform well at removing noise and outliers in the dataset, while avoiding overfitting caused by the high dimensionality of gene expression data [19]. In most conventional studies, the genes of interest are evaluated through their expression values in a case-control study, where a set of genes with expression that varied in one class, as compared to others, is selected. Numerous statistical models and tests have been developed with the aim of identifying the most significant set of candidates. However, statistical methods only focus on univariate comparison, and the importance of the gene-gene relationship is often neglected. On the contrary, other than predicting outcomes for classes to improve the performance of a model, machine learning can be used to select relevant features by looking into the intrinsic intervariable relationships of the genes. In AD studies, machine learning methods have been applied to select differential miRNA biomarkers that exhibit similar structural and functional patterns [20]. The recent trend of machine learning in miRNA expression studies in AD mainly focuses on selecting a small set of miRNAs from a group of differentiated miRNAs to obtain more precise and reliable results of association [20; 21]. The present case-control study focuses on investigating the differential miRNAs in the peripheral blood of Malaysian AD patients. The population in Malaysia is multi-ethnic and exposed to multicultural environments. Hence, this may result in differences not seen in the findings of studies on monoethnic populations, such as Caucasian, African, and Chinese. The present study started with a data-integration framework that applied statistical and machine learning techniques to identifying potential miRNA candidates that demonstrate differential expression in AD patients as compared with controls. Problems caused by the high dimensionality of the dataset were minimized by conducting a two-step machine learning method in which supervised feature-selection and unsupervised clustering were carried out. In addition, the potential roles of the miRNA candidates in AD pathogenesis were correlated with the functions of their respective targeted mRNAs (genes) by carrying out miRNA target gene prediction. The pathways involved with the identified miRNAs and genes, together with their roles in AD, were discussed in an attempt to reach a more complete understanding of AD development. The remainder of this paper is structured as follows: Section 2 discusses the existing methods used in the study of AD and the research gaps that need to be filled. Section 3 explains the materials used and the integration framework proposed in this study. Section 4 presents the results, and the findings are discussed in Section 5. Finally, conclusions are drawn and the challenges of this study are highlighted in Section 6.