# Tabular Data Generation to Improve Classification of Liver Disease Diagnosis
## 2 Related Works
### Data Augmentation  
Increasing the variety of data samples is an excellent way to improve the performance of machine learning algorithms. This technology is predominantly utilized in computer vision applications. Simple algorithms can be used, such as transformations on existing images (flipping, cropping, and rotation) [18; 24]. However, these methods only modify the existing samples and do not generate new ones. To address this issue, researchers have begun adapting algorithms for increasing data more effectively in tabular data.  
Synthetic data is a type of data that is generated by combining real-world characteristics and sampling from a distribution. Each approach to generating synthetic data has its unique advantages and disadvantages. Historically, synthetic data has been used for data anonymization and software testing, an is sometimes even intertwined. With the rise of Big Data and deep learning, privacy has become an increasingly important concern, making creating realistic synthetic data a critical aspect. A study published in [16] implements an uncontrolled data enhancement method in a semi-supervised learning environment to make the model more consistent when training both real and unmarked data. This method replaces random noise with noise that is more like the real world. This makes it easier to predict what will happen based on real data and predictions based on data that has been improved. Another study [25] presents a data enhancement method for training a convolutional neural network (CNN) known as random erasing.  
However, this approach faces the challenge of occlusion, which is prevalent in using CNNs. To address this challenge, the study [24] proposes AutoAugment. This process identifies the most suitable data enhancement policy using reinforcement learning to find the optimal combination of decisions and feature order for maximum accuracy. Additionally, the authors of [26] propose the Fast AutoAugment algorithm, motivated by Bayesian data enhancement. This algorithm optimizes the search for missing data points and results in faster search times and improved error rates compared to AutoAugment. Meanwhile, the population-based increase (P.B.A.) algorithm replaces the fixed increase policy during training periods, producing the optimal schedule for each training period of increased policies. This algorithm requires less computing power and training time, as demonstrated in [27].  
The current research on liver disease diagnosis using machine learning primarily focuses on using existing datasets and applying different algorithms for prediction. However, there needs to be more knowledge about using newly generated tabular data to improve liver disease diagnosis. This type of research would involve creating new datasets from relevant sources and using these datasets to train machine learning models for improved predictions. Creating these new datasets could involve data pre-processing, feature selection, and data generation techniques such as synthetic data generation and oversampling to overcome class imbalance issues. The use of newly generated tabular data has the potential to provide more accurate and robust predictions in liver disease diagnosis as compared to the use of existing datasets. Further research in this area can help fill this knowledge gap and advance the field of liver disease diagnosis using machine learning.