# Optimizing DNA Sequence Classification via a Deep Learning Hybrid of LSTM and CNN Architecture
## 6 Analysis of Consolidated Deep Learning and Machine Learning  
Besides the above-discussed models, future iterations of this study will involve sophisticated models such as transformer-based classifiers (e.g., DNABERT), hybrid GRU-BiLSTM networks, and attention-based CNNs. These showed improved performance in more recent studies. Such comparisons will enable a stronger benchmarking of the developed hybrid model and bring out the relative performance of the model with varying architectures.  
The combined outcomes from machine learning and deep learning in Table 7 enunciate the picture very clearly regarding the performance trend. The hybrid LSTM + CNN model shows the best performance consistently, with the highest accuracy of 100%, reflecting its superior ability to integrate sequential and spatial features for complex DNA classification tasks. This hybrid architecture outperforms both traditional machine learning models and other deep learning approaches significantly  
The machine learning algorithms are topped by XGBoost, which has an accuracy of 81.50%, showing that it is very successful in structured data and complicated patterns. It is followed closely by k-nearest neighbor, which manages to attain an accuracy of 70.77%, slightly outperforming random forest, whose best performance is 69.89%. These models give quite good baselines for DNA sequence classification but remain far behind the results of deep learning.  
The deep learning shown in Figure 13, such as the Deepsea model, contributes significantly to deep learning with an accuracy of 76.59%, underpinning its strength in leveraging convolutional neural networks for feature extraction. Other deep learning models, such as individual dimensionality reduction, perform at 68.15%, and Deep Variant at 67.00%, thus far performing on par with the performance of machine learning models.  
These again hint at the potential but also the limitations of these models in fully capturing DNA sequence complexities. The transformation model with 43.75% and the graph neural network with 30.71% are among the weakest in the deep learning category, showing room for improvement, especially in feature extraction and handling complex dependencies in DNA sequences. On the other hand, traditional models like logistic regression with 45.31% and naive Bayes with 17.80% do not reflect good performance in comparison; this emphasizes the challenge of using a simple model on highly nonlinear and complicated DNA sequence data. Their relatively low performance underlines the necessity of a more advanced deep-learning architecture for such tasks.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Rank** & **Model** & **Accuracy (\%)** & **Category** \\ \hline
1 & Hybrid LSTM+CNN & 100.00 & Deep Learning \\ \hline
2 & XGBoost & 81.50 & Machine Learning \\ \hline
3 & K-Nearest Neighbor & 70.77 & Machine Learning \\ \hline
4 & Random Forest & 69.89 & Machine Learning \\ \hline
5 & DeepSea & 76.59 & Deep Learning \\ \hline
6 & Dimensionality Reduction & 68.15 & Deep Learning \\ \hline
7 & DeepVariant & 67.00 & Deep Learning \\ \hline
8 & Logistic Regression & 45.31 & Machine Learning \\ \hline
9 & Transformation Model & 43.75 & Deep Learning \\ \hline
10 & Graph Neural Network & 30.71 & Deep Learning \\ \hline
11 & Naïve Bayes & 17.80 & Machine Learning \\ \hline \hline \end{tabular}
\end{table}
Table 7: Analysis of consolidated deep learning and machine learning results.