## 2 Materials and Methods
### Machine Learning Approach
#### 2.3.4 Classification
\(x_{l}\): the unknown sample  
Step 1: for i = 1 to n, perform the above steps  
Step 2: calculate the distance d (xi, x)  
Step 3: Finish for  
Step 4: Repeat Step 1 while keeping the indices for the K shortest distances d (\(x_{l}\), x) in mind  
Step 5: Turn the majority label over. We found that small values of k gave better results, so ranging k from 1 to 9 does not influence the accuracy significantly. The highest outcome is 91.67%, much higher than the one obtained by the SVM. Random Forest (RF) [32] relies on a heuristic partitioning of the description space. The decision structure of a tree is built on the recursive division of this space, making the ultimate decisions highly dependent on the upstream divisions. The search space of possible decision tree structures is then strongly constrained by these dependencies. A random forest consists of a basic classifier set such as a decision tree presented in:  
\[\{h(x,\Theta_{k}),\ k=\ 1,\ldots\L\}\]  
Random forests consist of a collection of binary decision trees into which randomness has been introduced. RF was defined by [PERSON] et al. [33] with the following broad definition. Consider (\(\hat{h}(\Theta_{1}),\ldots,\hat{h}(\Theta_{q})\)) a set of tree predictors, with \(\Theta_{1},\ldots,\Theta_{q}\) independent random variables of \(\Theta_{n}\). The random forest predictor \(\hat{h}_{RF}\) is acquired by aggregating this set of random trees in the following way:  
\[\hat{h}_{RF}\ (x)=\ \frac{1}{q}\ \sum_{l=1}^{q}\hat{h}(x,\Theta_{l}) \tag{24}\]  
The average prediction of the individual trees in the regression. \[\hat{h}_{\text{RF}}\ (\text{x})=\ \text{argmax}_{1\leq\text{k}\leq\text{K}}\ \sum_{l=1}^{q}1_{\hat{h}(\text{x},\Theta_{l})=\text{k}} \tag{25}\]  
A majority vote among the classification's prediction trees. The term random forest arises from considering that individual predictors here are explicitly per-tree predictors as well as each tree relies on a supplementary random variable. Using RF, we achieved better and greater accuracy over other machine learning algorithms 97.54%. Naive Bayes (NB) [34]: The Naive Bayes algorithm represents one of the most straightforward and efficient ways of classifying. This classifier relies on the Bayesian network concept, which stands for a possible graphical model featuring a random set of variables along with their conditional autonomy. There are many efficient algorithms in Bayesian networks that perform assimilation and learning. The only prerequisite is that the features of the data operate independently. Data features are interdependent owing to their genetic roots. However, the dependence does not seem to be stout. Consequently, classification algorithms may assume that the features are independent and use the Naive Bayes approach. It first generates probabilities for each sequence in the data, then calculates the probable sequence number for each sample and compares it to the number of occurrences in the database. The sample is then ranked based on the number of probable sequences. The Naive Bayes algorithm is a simple probabilistic classifier that uses the Bayes theory and assumes a high level of independence among the data features. The data storage possibilities for X with a category label \(C_{j}\) are:  
\[P\big{(}C_{j}\backslash X\big{)}=\frac{P\big{(}X\backslash C_{j}\big{)}*P \big{(}C_{j}\big{)}}{P(X)} \tag{26}\]By applying this classifier to our dataset, we obtained the least precision of 60.09% compared to the other classifiers. Latent Dirichlet Allocation (LDA) [35] is part of the ML toolkit used to explain sets of observations through the identification of unobserved groups defined by similarities in the data. Using this model, we obtained an accuracy of 80.28%. Classification And Regression Trees (CART) [36] can be applied to solve classification and regression predictive modelling problems. It serves as the foundation for essential algorithms including random forest, bagged, and boosted decision trees.