## 3 Results
### Study Characteristics
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Cites** & **Year** & **ML Algorithm** & **ML Dataset Source** & **Contibution** \\ \hline
[118] & 33 & 2018 & BN, kNN, MPL, and J48 DT & Not Specified & It compares and analyzes various machine learning algorithms used for DGA fault classification, i.e., Bayes network, multilayer perceptron, k-Nearest Neighbor, and J48 decision tree. It further assesses the impact of applying the AdaBoost algorithm and bootstrap aggregation (bagging) on the performances of these algorithms. The J48 decision tree algorithm was the best-performing algorithm and achieved an accuracy of 94.5\%. \\ \hline
[119] & 16 & 2020 & ANN-GSA and SVM-GSA & Not Specified & It proposes a hybrid feature selection technique for determining fault types from DGA data using a Gravitational Search Algorithm. The proposed model achieved an accuracy of 98\%. \\ \hline
[120] & 25 & 2019 & Fuzzy inference system and ANN & Literature & It proposes a multiple fault detection technique to improve the conventional interpretation methods to also cater for multiple fault diagnosis in non-binary situations. The proposed model achieved an accuracy of 97\%. \\ \hline
[121] & 6 & 2015 & Hybrid fuzzy logic & IEC TC10 and Substitution Equipment Maintenance and Management Web System of Taiwan Power Company & It utilizes a hybrid fuzzy technology to integrate five conventional dissolved gas analysis interpretation approaches for transformer fault diagnosis. The model achieved an accuracy of 94.36\%. \\ \hline
[122] & 3 & 2021 & Modified Clustering & Not Specified & It proposes an unsupervised methodology for diagnosing transformer conditions using a modified clustering technique to classify the condition of transformers using DGA datasets. \\ \hline
[123] & 3 & 2022 & Improved Harris Hawks & Literature & It proposes a fault diagnosis method based on improved Harris Hawks optimized random forest using logistic chaotic mapping to adapt the key parameters, non-linear energy factor to control the algorithm to transition, and Gaussian Mutation for strengthening the searchability. The proposed model achieved an accuracy of 90.18\%. \\ \hline
[124] & 13 & 2022 & HHO-KELM & IEC TC10 & It presents a fault diagnosis based on the Harris Hawks optimization algorithm optimized kernel extreme learning machine, this optimization enhances the fault diagnostic performance of the model which achieved an accuracy of 95.70\%. \\ \hline
[125] & 30 & 2015 & FCM-GA-BPNN & Provincial Power Company of State Grid Corporation & It integrates a combination of the conventional IEC and Doernerning interpretation techniques with the genetic algorithm and fuzzy c-means clustering algorithm (FCM) optimized BP for improved diagnosis accuracy and generalization ability while overcoming issues associated with data noises, over-fitting, and convergence. The proposed model achieved an accuracy of 99.71\%. \\ \hline
[126] & 17 & 2019 & SVM and kNN & KEFCO & It presents a classification system of transformer faults and defects based on transformer parts and structures using the ages obtained from the DGA datasets and applied to support vector machine and k-Nearest Neighbors algorithms. The diagnostic performance of this proposed model achieved an accuracy of 88\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 6. Cont.