## 1 Introduction  
Machine learning (ML) involves making predictions based on the data provided to the computer [1], and using this concept of ML, machines can be trained to respond to new information without human interaction. Deep learning (DL) is a type of artificial intelligence and ML that mimics human behavior when used with certain data. It is a type of ML that enables computers to evaluate enormous amounts of data and generate preliminary findings that may subsequently be used to support any response [2]. DL, a subfield of artificial intelligence, works with algorithms that are influenced by the structure and function of the brain. Artificial neural networks with a second hidden layer are used in DL. Cascading neural networks are used to process non-linear data sets. DL is a method that searches through numerous buried data layers to find the key. It can be used to diagnose other illnesses. Due to DL, the healthcare sector may now review data quickly and accurately [3]. There could be a backbone abnormality called Kyphosis (spine). A toddler either has a humpback from birth or is predisposed to developing one due to abnormal medical conditions. There will be anguish and distortion because of the extreme humpback. Kyphosis, a spinal curve that denotes an excessively rounded back, can emerge from stress, infectious disease, aberrant development, genetic disease, and iatrogenic disease on occasion [4]. How Kyphosis is handled depends on several factors, including age, the cause of the curve, and its effects. Kyphosis disease in children should be more thoroughly diagnosed at a young age to prevent unusual spinal and vertebral problems. It is more crucial to recognize that the diagnosis of Kyphosis disease in children at an early stage may prevent issues with deformed spinal vertebrae [5]. The Mayo Clinic has more information on Kyphosis at ([[https://www.mayoclinic.org/diseases-conditions/kyphosis/symptoms-causes/syc-20374205](https://www.mayoclinic.org/diseases-conditions/kyphosis/symptoms-causes/syc-20374205)]([https://www.mayoclinic.org/diseases-conditions/kyphosis/symptoms-causes/syc-20374205](https://www.mayoclinic.org/diseases-conditions/kyphosis/symptoms-causes/syc-20374205)), accessed on 1 December 2022). Medical diagnosis is a vital yet difficult task that must be conducted correctly and consistently; therefore, automating it would be advantageous [6]. Hyperparameter tuning is finding the perfect model architecture by adjusting the parameters that determine the model architecture, called Hyperparameters. Hyperparameter adjustment is essential to govern ML's or DL's general behavior [7]. A model parameter known as a \"Hyperparameter\" is one whose value affects learning and cannot be inferred from training data. Before beginning the process of training and learning the model, Hyperparameters are externally specified. Finding a set of ideal Hyperparameter values for a learning algorithm and using this tuned algorithm on any data set is called Hyperparameter tuning. A Hyperparameter governs the learning process, and as a result, the values of these parameters directly impact other model parameters such as weights and biases, which in turn affect how well the model performs. By tuning these Hyperparameters, any ML or DL model's accuracy is frequently increased [8]. By comparing and using various ML algorithms, alternative medical practitioners have already done a respectable job detecting and forecasting diseases. According to [PERSON] et al., Kyphosis must be treated as some cases are congenital and some are iatrogenic, depending on the patient's needs and current condition [4]. In [5], Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Random Forest (RF) were utilized to recognize and predict Kyphosis disease. After applying grid search strategies, they found that the ANN performed the best, with 86.42% and 85.19% supporting 10-fold and 5-fold cross-validations, respectively.