[Retracted] Medicolite‐Machine Learning‐Based Patient Care Model

Although Kyphosis, an excessive forward rounding of the upper back, can occur at any age, adolescence is the most common time for Kyphosis. Surgery is frequently performed on Kyphosis patients; however, the condition may persist after the operation. The tricky part is figuring out, based on the patient’s traits, if the Kyphosis condition will continue after the treatment. There have been numerous models employed in the past to predict the Kyphosis disease, including Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Deep Neural Network (DNN), and others. Unfortunately, the precision was overestimated. Based on the dataset received from Kaggle, we investigated how to predict Kyphosis disorders more accurately by using these models with Hyperparameter tuning. While the calculations were being performed, certain variables were modified. The accuracy was increased by optimizing the fit parameters based on Hyperparameter tuning. Accuracy, recall or sensitivity, specificity, precision, balanced accuracy score, F1 score, and AUC-ROC score of all models, including the Hyperparameter tuning, were compared. Overall, the Hyperparameter-tuned DNN models excelled over the other models. The DNN models’ accuracy was 87.72% with 5-fold cross-validation and 87.64% with 10-fold cross-validation. It is advised that when a patient has a clinical procedure, the DNN model be trained to detect and foresee Kyphosis disease. Medical experts can use this study’s findings to correctly predict if a patient will still have Kyphosis after surgery. We propose that deep learning should be adopted and utilized as a crucial and necessary tool throughout the broad range of resolving biological queries.

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“…It can be used to diagnose other illnesses. Due to DL, the healthcare sector may now review data quickly and accurately [3].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection

Chauhan

Lilhore

Gupta³

et al. 2023

Applied Sciences

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“…Thanks to developments in data technologies like storage capacity, processing power, and data transmission rates, machine learning has been widely implemented in a variety of industries, including healthcare. Deep learning enables the healthcare industry to analyze data at lightning-fast speeds while maintaining excellent accuracy [4].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection

Chauhan¹,

Gupta

Garg

2022

Preprint

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The spinal curve that represents an abnormally rounded back is known as kyphosis, and it can be caused by stress, infectious disease, developmental abnormalities, genetic disease, and occasionally iatrogenic disease. Due to the fact that it provides technical explanations and a significant standard, the emergence of machine learning and deep learning has demonstrated that they can better characterize data. Machine Learning and Deep Learning are promising approaches that help in the prediction, diagnosing of sickness. Compared to conventional computing algorithms, deep learning algorithms are far more effective in disease detection and diagnosing. In this study, Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Deep Neural Network (DNN) models are applied to detect the presence of kyphosis in youngsters supported medicine information and compared to point out the rise in the potency of predictions associated with kyphosis. Each classification algorithm that makes use of the hyperparameter tuning and control feature enhances the algorithms' overall prediction performance, which enhances the algorithms' overall performance. The potency of the planned models were enforced and checked over medical dataset utilized from Kaggle. Overall, the DNN model performed best among the wide range of different models and achieved maximum accuracy and other performance metrics scores derived from the stratified K-Fold cross-validation. It is advised that after a patient has completed a clinical procedure, the DNN model be trained to identify and forecast kyphosis disease.

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