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Open AccessArticle

Special Issue on Using Machine Learning Algorithms in the Prediction of Kyphosis Disease: A Comparative Study

School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611371, China
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Appl. Sci. 2019, 9(16), 3322; https://doi.org/10.3390/app9163322
Received: 13 July 2019 / Revised: 9 August 2019 / Accepted: 10 August 2019 / Published: 13 August 2019
(This article belongs to the Special Issue Machine Learning for Biomedical Data Analysis)

Abstract

Machine learning (ML) is the technology that allows a computer system to learn from the environment, through re-iterative processes, and improve itself from experience. Recently, machine learning has gained massive attention across numerous fields, and is making it easy to model data extremely well, without the importance of using strong assumptions about the modeled system. The rise of machine learning has proven to better describe data as a result of providing both engineering solutions and an important benchmark. Therefore, in this current research work, we applied three different machine learning algorithms, which were, the Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Network (ANN) to predict kyphosis disease based on a biomedical data. At the initial stage of the experiments, we performed 5- and 10-Fold Cross-Validation using Logistic Regression as a baseline model to compare with our ML models without performing grid search. We then evaluated the models and compared their performances based on 5- and 10-Fold Cross-Validation after running grid search algorithms on the ML models. Among the Support Vector Machines, we experimented with the three kernels (Linear, Radial Basis Function (RBF), Polynomial). We observed overall accuracies of the models between 79%–85%, and 77%–86% based on the 5- and 10-Fold Cross-Validation, after running grid search respectively. Based on the 5- and 10-Fold Cross-Validation as evaluation metrics, the RF, SVM-RBF, and ANN models achieved accuracies more than 80%. The RF, SVM-RBF and ANN models outperformed the baseline model based on the 10-Fold Cross-Validation with grid search. Overall, in terms of accuracies, the ANN model outperformed all the other ML models, achieving 85.19% and 86.42% based on the 5- and 10-Fold Cross-Validation. We proposed that RF, SVM-RBF and ANN models should be used to detect and predict kyphosis disease after a patient had undergone surgery or operation. We suggest that machine learning should be adopted and used as an essential and critical tool across the maximum spectrum of answering biomedical questions. View Full-Text
Keywords: artificial intelligence; machine learning; random forest; support vector machine; artificial neural network; kernels; biomedical data; kyphosis artificial intelligence; machine learning; random forest; support vector machine; artificial neural network; kernels; biomedical data; kyphosis
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Dankwa, S.; Zheng, W. Special Issue on Using Machine Learning Algorithms in the Prediction of Kyphosis Disease: A Comparative Study. Appl. Sci. 2019, 9, 3322.

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