article · Sensors
This research developed a Bayesian Optimization-Support Vector Machine (BO-SVM) model to classify individuals with and without Parkinson's disease. The study aimed to improve classification accuracy by optimising the hyperparameters of six machine learning models, including SVM, Random Forest, Logistic Regression, Naive Bayes, Ridge Classifier, and Decision Tree, using Bayesian Optimization. A dataset with 23 features and 195 instances was used, and model performance was evaluated using accuracy, F1-score, recall, and precision. The SVM model consistently showed the best performance both before and after hyperparameter tuning, achieving an accuracy of 92.3% when optimised with Bayesian Optimization.
Accurately classifying Parkinson's disease is crucial for early diagnosis and intervention, which can significantly improve patient outcomes. This research offers an advanced machine learning approach that could enhance the reliability of diagnostic tools, helping healthcare professionals identify the condition more effectively.
This research presents an early-stage machine learning model for Parkinson's disease classification. It could form the basis for developing advanced diagnostic support tools for clinicians, aiding in the accurate identification of the disease. Potential users include medical diagnostic laboratories and healthcare providers. Further validation and integration into clinical workflows would be required for real-world application.
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Parkinson's disease (PD) has become widespread these days all over the world. PD affects the nervous system of the human and also affects a lot of human body parts that are connected via nerves. In order to make a classification for people who suffer from PD and who do not suffer from the disease, an advanced model called Bayesian Optimization-Support Vector Machine (BO-SVM) is presented in this paper for making the classification process. Bayesian Optimization (BO) is a hyperparameter tuning technique for optimizing the hyperparameters of machine learning models in order to obtain better accuracy. In this paper, BO is used to optimize the hyperparameters for six machine learning models, namely, Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Ridge Classifier (RC), and Decision Tree (DT). The dataset used in this study consists of 23 features and 195 instances. The class label of the target feature is 1 and 0, where 1 refers to the person suffering from PD and 0 refers to the person who does not suffer from PD. Four evaluation metrics, namely, accuracy, F1-score, recall, and precision were computed to evaluate the performance of the classification models used in this paper. The performance of the six machine learning models was tested on the dataset before and after the process of hyperparameter tuning. The experimental results demonstrated that the SVM model achieved the best results when compared with other machine learning models before and after the process of hyperparameter tuning, with an accuracy of 92.3% obtained using BO.
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DOI: 10.3390/s23042085
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