## 1 Introduction  
Artificial Intelligence (AI) techniques have been widely used to improve the quality of patient life and care through early diagnosis of disease. Such a technique has become popular because it not only can reduce cost-effectiveness and mortality, but also provides good predictions that facilitate precise treatment [1; 2; 3]. Cardiac Disease (CD) monitoring and detection, in particular, is a difficult task which requires identifying the patterns and interaction among variables using various techniques [2]. Despite these significant efforts, recently it has been shown that conventional methods based on a simple score do not perform well and the results obtained by such methods remain unsatisfactory to date. The application of computer-based methods is a potential solution that allows cardiologists to observe the CD in the long-term recording with the better-diagnosing result. However, to produce an optimal CD prediction requires some metrics such as sensitivity, specificity, and accuracy. These parameters help the cardiologist to predict outcomes accurately and effectively. They cannot only be evaluated using a simple score (i.e., probability and statistical method) or traditional CD risk factors (i.e., diabetes, hypertension, and smoking). Currently, Machine Learning (ML) algorithms can overcome the drawbacks of automatic learning to help build a recommendation system. The results can help a medical doctor or cardiologist in making more accurate and sensitive predictions [2; 3; 4]. The learning process is an important stage of ML algorithms in order to produce accurate diagnosis and prediction. It generally can be categorized into three types of learning processes: supervised, unsupervised, and reinforcement. In supervised learning, algorithms use a dataset labeled by experts. The algorithms develop a model to predict or classify future events or to determine which variables are most relevant to the outcome. ML with supervised learning produces excellent results in classification and regression problems [5]. However, it requires a lot of data to be labeled by humans, and it is time-consuming [5]. In contrast, unsupervised learning seeks to identify novel disease mechanisms from hidden patterns present in the data without feedback from humans. Unfortunately, it always produces some bias, due to in the initial cluster patterns not being validated with other groups of data [6]. The combination process between supervised and unsupervised learning is called reinforcement learning. However, to obtain high accuracy, the reinforcement learning process needs a trial and error phase, which is a very time-consuming, and it can also produce untrustworthy results [3; 5]. Hence, an improvement in the learning process to produce high performance with trusted values is desirable. There are three phases to process CD prediction using an ML algorithm: training, validation, and testing. Unfortunately, with less training data sets, CD prediction can lead to inaccurate predictions in the testing phase, meaning it can be biased and inaccurate [6]. In order to improve the diagnosis results, more data sets are required for training the model [2; 6]. Deep learning (DL) is one of the ML approaches which has been successfully implemented in a diverse range of biomedical fields with large datasets, as presented in references [7; 8]. This approach has become a promising field and has been proliferating in recent years [9; 10]. DL architecture mimics human brain operation using multiple layers of the artificial neuron for generating automated predictions [5; 9; 10]. The DL architecture with an unsupervised learning approach has been widely implemented.