A modified projective forward-backward splitting algorithm for variational inclusion problems to predict Parkinson's disease

This research studies variational inclusion problems, which is a branch of optimization. A modified projective forward-backward splitting algorithm is constructed to solve this problem. The algorithm adds the inertial technique for speeding up the convergence, and the projective method for several r...

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Bibliographic Details
Published in:Applied mathematics in science and engineering Vol. 32; no. 1
Main Authors: Cholamjiak, Watcharaporn, Das, Shilpa
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis Ltd 31.12.2024
Taylor & Francis Group
Subjects:
46T99
Accuracy
Algorithms
extreme learning machines (ELM)
forward-backward splitting algorithm
Machine learning
Parkinson's disease
Performance evaluation
Recall
Regularization
regularized least squares
Splitting
Variational inclusion problem
ISSN:2769-0911, 2769-0911
Online Access:Get full text
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Summary:This research studies variational inclusion problems, which is a branch of optimization. A modified projective forward-backward splitting algorithm is constructed to solve this problem. The algorithm adds the inertial technique for speeding up the convergence, and the projective method for several regularization machine learning models to meet good model fitting. To evaluate the performance of the classification models employed in this research, four evaluation metrics are computed: accuracy, F1-score, recall, and precision. The highest performance value of 92.86% accuracy, 62.50% precision, 100% recall, and 76.92% F1-score shows that our algorithm performs better than the other machine learning models.
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ISSN:2769-0911
2769-0911
DOI:10.1080/27690911.2024.2314650