## 3 Results
### Study Characteristics
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Cites** & **Year** & **ML Algorithm** & **ML Dataset Source** & **Continuation** \\ \hline
[20] & 145 & 2014 & \
\begin{tabular}{c} Adaptive Neuro-Fuzzy \\ Inference System \\ \end{tabular} & IEC TC10 &
\begin{tabular}{c} It develops an Adaptive Neuro-Fuzzy Inference System model to identify transformer incipient faults using DGA. The model achieved an accuracy of 98\% when applied to the IEC standard, 97\% on the Rogers ratio method, and 99\% on the Doemenburg ratio method. \\ \end{tabular} \\ \hline
[21] & 73 & 2019 & Improved Kill Herd SVM & \
\begin{tabular}{c} IEC TC10 and \\ Private Company \\ \end{tabular} &
\begin{tabular}{c} It proposes an Improved Kill Herd Optimized SVM algorithm based on cross-validation principles to improve fault diagnosis accuracy. The accuracy of the proposed model reaches 85.71\%. \\ \end{tabular} \\ \hline
[22] & 2 & 2024 & Deep CNN & Literature &
\begin{tabular}{c} It presents an asset management technique for classifying incipient faults and predicting the health index of transformers using deep CNN; the model achieves 87\% accuracy. \\ \end{tabular} \\ \hline
[23] & 4 & 2022 & kNN, DT & New York Power Authority &
\begin{tabular}{c} It presents a fault detection model using kNN. It applies the key gas method to detect outliers, and then the Basic Gas Ratio is used to detect faults, the accuracy of this model reaches 88\%. \\ \end{tabular} \\ \hline
[24] & 178 & 2017 & Deep believe network & \
\begin{tabular}{c} State Grid Corporation of \\ China and Literature \\ \end{tabular} &
\begin{tabular}{c} It presents a fault diagnosis method based on the deep believe networks algorithm which \\ assesses the transformer health index including the impact of discharge and overheating. \\ This model reaches an accuracy of 90\%. \\ \end{tabular} \\ \hline
[25] & 90 & 2019 & Hidden Markov model & \
\begin{tabular}{c} State Grid Zhejiang Electric \\ Power Co. Ltd. \\ \end{tabular} &
\begin{tabular}{c} It develops a dynamic early prediction and warning model which combines the Gaussian mixture model with the Hidden Markov model. The proposed model achieved an accuracy of 95\%. \\ \end{tabular} \\ \hline
[20] & 34 & 2020 & Lasso regression & \
\begin{tabular}{c} State Grid Zhejiang Electric \\ Power Co. Ltd. \\ \end{tabular} &
\begin{tabular}{c} It uses the Pearson correlation coefficient to filter and optimize the feature set to reduce the redundancy of the selected features and applies Lasso regression to build a multi-dimensional linear model of the selected features. The proposed model achieved an accuracy of 87.5\%. \\ \end{tabular} \\ \hline
[27] & 66 & 2021 & \begin{tabular}{c} Teaching-learning \\ based optimization \\ \end{tabular} & \
\begin{tabular}{c} Egyptian Electricity \\ Holding Company \\ \end{tabular} &
\begin{tabular}{c} It develops an efficient teaching-learning-based optimization (TLBO) model to enhance the diagnostic accuracy of the transformer faults based on gas concentration percentages limits and gas ratios. The developed model achieved an accuracy of 86.27\%. \\ \end{tabular} \\ \hline
[28] & 19 & 2021 & \
\begin{tabular}{c} Gaussian process \\ multi-classification \\ \end{tabular} & General Electric &
\begin{tabular}{c} It proposes a Gaussian process multi-classification model to enhance and produce a probabilistic interpretation of the analysis results. The proposed model achieved an accuracy of 95\%. \\ \end{tabular} \\ \hline
[29] & 5 & 2023 & k-means clustering algorithm & IEC TC10 and Literature &
\begin{tabular}{c} It proposes a two-step hybrid method consisting of a k-Means Clustering algorithm based on the generic algorithm for subset analysis. The proposed model achieved a diagnostic accuracy of 89.28\%. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Transformer DGA-based fault classification literature survey.