## 4 Discussion
* Transformer age: Very few of the studied papers specified the age of the transformers from which the oil samples were taken; this could have helped provide insight into the relationship between transformer age and its gas concentration performance based on DGA sample results. * Lack of data preprocessing and feature extraction techniques: Some of the papers do not specify the techniques used for data preprocessing and feature techniques; this could have helped to provide the readers with insights as to which data preprocessing and feature extraction techniques are the most effective in relation with the machine learning algorithms employed. To optimize the performance of machine learning models, before processing raw datasets for training, it is a pre-requisite for them to be thoroughly cleaned by removing as much noise and redundant information as possible so that only useful dataset attributes are evaluated [144; 145]. This process is essential to eliminate the inconsistent and unwanted noise often hidden in the input raw data [146; 147]. In addition to data preprocessing, another critical step required for data preparation is feature extraction. Feature extraction is a dimension-reduction technique used to scale down datasets by eliminating non-essential features and properties in datasets used to train machine learning models and include only key, important features from the raw datasets to help narrow the focus of the algorithms towards selected features and obtain the final class of the features after eliminating unwanted and non-essential features [144; 146; 148]. In general, feature extraction identifies the characteristic features that better represent the dataset by minimizing the within-class pattern variability while improving the between-class variability and preventing dimensionality [146; 147; 148; 149]. However, studies have indicated the issues associated with raw data preprocessing and feature extraction inclusion in machine learning models. These issues include navigating around large datasets with many categorized features and expanding feature sets which all serve as stumbling blocks for researchers when designing models as these issues lead to long computational and processing times of models [150]. As a result, researchers refrain from disclosing to what extent data pre-processing and feature extraction were conducted for their trained models to overcome the risk of dimensionality and overfitting. * Lack of specifications used: A large number of the papers studied do not specify the globally adopted industrial standards used in the analysis. This causes concern as having an accurate depiction of standards would aid in understanding which ones are the most effective and accurate. * Lack of distinction in the DGA gases studies: The studied papers apply a holistic approach when describing the gases studies, with almost all of them mentioning all gases, i.e., hydrogen (H\({}_{2}\)), methane (CH\({}_{4}\)), ethylene (C\({}_{2}\)H\({}_{4}\)), ethane (C\({}_{2}\)H\({}_{6}\)), acetylene (C\({}_{2}\)H\({}_{2}\)), carbon monoxide (CO), and carbon dioxide (CO\({}_{2}\)). From the shortcomings and limitations identified in the literature and listed above, the instances in which the authors did not report critical elements of their study, which may have been deemed as not essential by them, compromised the analysis and understanding of readers and presented a potential gap in reporting and documentation essential information.