## 1 Introduction  
Deep learning computational models consist of many processing layers in order to learn representations of data with many levels of abstraction [1]. Deep learning is a machine learning mechanism that employs many layers of nonlinear information processing for the purposes of supervised or unsupervised learning, feature extraction, and classification. Deep learning algorithms depend heavily on the number of data and computing power [2; 3; 4]. Accordingly, they do not perform to an optimal level with small datasets. Recent advances in terms of adequate amounts of collected data and increased levels of computing power [2] have led to a resurgence in neural network research, and this has in turn sparked a new era of (deep) machine learning research. Limited dataset sizes are considered to be one of the most critical concerns in terms of early prediction [5]. Unfortunately, this issue is particularly common in the case of diseases such as rare hereditary diseases (e.g., Maple syrup urine disease [6]), unique study populations (e.g., Kashin Beck Disease (KBD) [7]), individually-tailored treatments (e.g., tumour treatment [8]), isolated environments (e.g., Ebola [9]), emergency situations (e.g., autoimmune systemic diseases and vasculitides [10; 11]), and neurological or psychiatric disorders [12; 13; 14]. The size of available clinical samples is ordinarily small in the field of medical research due to the intrinsic prevalence of disorders and other factors such as elevated costs for patient recruitment and the limited time available for evaluations. Small sample sizes significantly limit the ability of pattern recognition methods to predict or classify individuals of different groups correctly, and this leads to inaccurate classification performance. Indeed, supervised classification methods require a training dataset in order to learn the classification algorithm that best differentiates the two groups and a testing dataset in order to verify the classification performance on previously unseen data. In medical research, samples and datasets are usually not large enough to perform the training phase and the testing phase of the computerized algorithm on a totally independent dataset. This renders this methodology prone to over fitting. Nevertheless, different methods have been proposed to overcome this critical issue. The main three methods are Mega Trend Diffusion (MTD), Functional Virtual Population (FVP), and Multivariate Normal synthetic sample generation (MVN). * Mega Trend Diffusion (MTD): MTD was proposed in [15] based on the information diffusion method that uses fuzzy theories to fill missing data [16; 17]. The main difference between MTD and the information diffusion method is that MTD employs a general diffusion function to scatter a collection of data across the whole dataset, while the information diffusion method diffuses each sample separately [17]. MTD merges mega diffusion with data trend estimation to control the symmetrical expansion issue and to increase the learning accuracy in flexible manufacturing system scheduling. Both the diffusion-neural-network and megatrend-diffusion require membership function values such as extra information, and that means the appearance possibility for each input attribute and the number of input attributes needed for artificial neural network training. This process makes the calculation more complicated and lengthy in duration. In addition, the membership function values basically do not hold any managerial meaning [15; 18]. Lastly, the MTD technique is applied to simulated systems. Therefore, it is not clear what this technique achieves in real system situations [19]. * Functional Virtual Population (FVP): The functional virtual population was developed in [20]. FVP is based on data domain expansion methods (i.e., left, right, and both sides) for small datasets [17]. The FVP method operates by adding virtual samples for training assistance and acquiring scheduling knowledge in dynamic industrializing systems.