ArticleSensors (Basel, Switzerland)2023
Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 106 citations in OpenAlex.
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- Novel machine-learning model for predicting lymph node metastasis in resectable pancreatic ductal adenocarcinoma.Annals of gastroenterological surgery · 2025Article
- Classification of psychiatry clinical notes by diagnosis: a deep learning and machine learning approach.PeerJ. Computer science · 2025Article
- Artificial intelligence in pediatric allergy research.European journal of pediatrics · 2024Review
- A unified approach for Parkinson's disease recognition: imbalance mitigation and grid search optimized boosting with LightGBM.Medical & biological engineering & computing · 2024Article
- Exploring factors influencing farmers' health self-assessment in China based on the LASSO method.BMC public health · 2024Article
- Optimizing classification of diseases through language model analysis of symptoms.Scientific reports · 2024Article
- Utility of four machine learning approaches for identifying ulcerative colitis and Crohn's disease.Heliyon · 2024Article
- Machine Learning-Based Predictive Modeling of Diabetic Nephropathy in Type 2 Diabetes Using Integrated Biomarkers: A Single-Center Retrospective Study.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
- An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction.Biomimetics (Basel, Switzerland) · 2023Article
- Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework.Diagnostics (Basel, Switzerland) · 2023Article
- Application of Machine Learning to Predict COVID-19 Spread via an Optimized BPSO Model.Biomimetics (Basel, Switzerland) · 2023Article
- Cautious Bayesian Optimization: A Line Tracker Case Study.Sensors (Basel, Switzerland) · 2023Article
- Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images.Scientific reports · 2023Article
- Machine learning models of Alzheimer's disease spectrum using blood tests.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.