## 1 Introduction
[PERSON]'s group examined the relationship between front postural stability, round shoulders, and progressive Kyphosis disease [7]. According to [9], artificial intelligence and ML have significantly advanced the field of spine research. In this review, the author covered several decision-support systems, computer-aided diagnoses, and issues. ML performed brilliantly and showed great promise in spine research [10]. ML could assist clinical staff in improving patient care, boosting output, and reducing unfavorable outcomes. ANN is frequently utilized in diagnosis and care system claims [11]. In the study [12], they covered how to forecast the clinical expenses of spinal fusion using the Naive Bayes (NB), SVM, Logistic Regression (LR), Decision Tree(DT), and RF models. They concluded that the RF model performed the best in terms of prediction, with an accuracy of 84.30% compared to the other methods. [PERSON] et al. [13] employed the RF and K-Nearest Neighbors (KNN) models to find spinal abnormalities. They found that, with an accuracy of 85.32%, the KNN model outperformed the RF model. In work [14], they use the SVM, LR, and bagged SVM and LR models, all of which are openly available in the Kaggle repository. The outcomes demonstrated that on the test dataset, SVM, LR, and bagged SVM and LR all performed similarly well, with an accuracy of 86.96%. However, SVM's superior recall value and miss rate set it apart from the competitors. Many people undergo Kyphosis surgeries, although the illness may persist after the operation. Therefore, the issue is to predict whether or not the patient will still have Kyphosis after the treatment based on the patient's many attributes. To classify the disease of Kyphosis, supervised learning systems are the main topic. Thus, in the present study, we apply Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Deep Neural Network (DNN) algorithms based on a biomedical dataset to build models to predict the absence or presence of a Kyphosis disease based on historical healthcare and personal records of Kyphosis disease patients after they have undergone surgery. Even though various models, including LR, NB, RF, KNN, SVM, ANN, etc., have been employed to predict the Kyphosis disease, regrettably, up until recently, it was virtually impossible to increase the accuracy. Therefore, by using these supervised models with Hyperparameter tuning, this work will examine Kyphosis disorder prediction using the dataset received from Kaggle. To choose the most efficient parameters, Hyperparameter tuning was done. While the calculations were being performed, certain variables were modified. The model's performance is maximized using that set of Hyperparameters. Then, evaluation and comparison of the model outputs were performed. The study examines the performance of supervised machine learning and deep learning (ML/DL) models in Kyphosis diagnosis across datasets to understand which model performs the best. Accuracy, recall or sensitivity, specificity, precision, balanced accuracy score, F1 score, and AUC-ROC score of all models, including the Hyperparameter tuning, were compared. In this study, we investigated the performance of tuned Hyperparameters and stratified K-fold cross-validation on the most popular ML and DL algorithms: LR, NB, RF, SVM, KNN, and DNN. This research aims to show the biomedical community how ML and DL algorithms have been used to categorize and forecast Kyphosis disease using biomedical data. The complete article is organized as follows: Section 2 covers the materials and methods, Section 3 covers the implementation results, Section 4 covers the discussion, and Section 5 covers the conclusion and future works.