## 2 Materials and Methods
### Classification Learning Algorithms  
A computer program is taught to make new observations or classifications depending on the given data through machine learning, deep learning, and statistical category. The proposed training and result prediction models utilized the learning methods LR, NB, RF, SVM, KNN, and DNN. Machine learning techniques such as logistic regression are used to solve classification issues. It is a predictive analysis technique that is based on the idea of probability. The output of a categorical dependent variable is predicted by logistic regression. The outcome must therefore have a discrete or absolute value. Rather than delivering the exact answer as 0 or 1, it provides the probability values that fall between 0 and 1. It might be either yes or no, 0 or 1, true or false, etc. Probability is determined in logistic regression using either the logistic function or the sigmoid function. The logistic function, a simple S-shaped curve, converts input into a value between 0 and 1. A logistic regression model mathematically predicts P(Y = 1) as a function of X [15; 16], presented in Equation (1). Where \(h\phi(x)\) define the output of the logistic function, \(\beta_{1}\) is the slope, \(\beta_{0}\) is the y-intercept, and X is the independent variable. \[h\phi(x)=\frac{1}{\left[\left(1+e^{-\left(\beta_{0}+\beta_{1}\right)}\ \ast X \right.\text{for}\ 0\leq h\phi(x)\geq 1\right. \tag{1}\]  
The NB algorithm is a supervised learning method that addresses classification problems based on the Bayes theorem. As a probabilistic classifier, it makes predictions based on the likelihood of an object [17; 18]. The Bayes theorem, often called Bayes' Rule, is a formula for determining how likely a hypothesis is given certain information. What happens will depend on the conditional probability. The Bayes theorem is expressed as defined in Equation (2). \[P(A|B)=\frac{P(B|A)\ast P(A)}{P(B)} \tag{2}\]  
Whereas the probability that a particular hypothesis (\(A\)) will happen is expressed as the posterior probability or \(P(A\,|\,B)\), \(P(B\,|\,A)\) stands for Likelihood Probability, which quantifies the likelihood that a given hypothesis is true based on the available data; the probability of a theory before viewing the evidence is known as priority probability, or \(P(A)\). \(P(B)\) stands for the probability of a marginal event. The well-known ML algorithm RF is a component of the supervised learning approach. It is probably a classifier that uses a collection of selection bushes on different subsets of the provided dataset to increase its predicted accuracy. Instead of relying exclusively on one selection tree, it anticipates the final phrase output using predictions from each tree and most predictions [19; 20]. Rather than relying on just one, it uses forecasts from all the trees to predict the result based on which predictions earned the most votes. To improve the predicted accuracy of the dataset, the random forest classifier, as its name suggests, \"Contains several decision trees on diverse subsets of the input dataset and takes the average\". \begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & **Explanation** \\ \hline Kyphosis & Whether or not the Kyphosis issue persisted after the procedure \\ \hline Age & Monthly age of the patient \\ \hline Number & Involved vertebrae within the procedure \\ \hline Start & The count of the surgery’s first or highest verteb that was affected \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of Dataset. The most popular use of the supervised, linear ML method known as SVM is the best fit for classification problems. This method makes it easier to solve the nonlinearity of the problem using the kernel method [21]. It aids in categorizing the data into two or more groups by using a boundary to distinguish between similar groups.