Adaptive Hyperparameter Fine-Tuning for Boosting the Robustness and Quality of the Particle Swarm Optimization Algorithm for Non-Linear RBF Neural Network Modelling and Its Applications

Ahmad, Zohaib; Li, Jianqiang; Mahmood, Tariq

doi:10.3390/math11010242

Cited by 14 publications

(8 citation statements)

References 39 publications

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“…Recent studies aiming to increase the accuracy of the diagnosis systems indicate that hyperparameter optimization techniques proved powerful tools. In [21], a radial basis function neural network (RBFNN) designed to identify and diagnose non-linear systems is proposed. The hyperparameters of the RBFNN are computed using PSO-based techniques.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study

et al. 2023

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Cardiovascular diseases (CVDs) are the leading cause of death globally. Detecting this kind of disease represents the principal concern of many scientists, and techniques belonging to various fields have been developed to attain accurate predictions. The aim of the paper is to investigate the potential of the classical, evolutionary, and deep learning-based methods to diagnose CVDs and to introduce a couple of complex hybrid techniques that combine hyper-parameter optimization algorithms with two of the most successful classification procedures: support vector machines (SVMs) and Long Short-Term Memory (LSTM) neural networks. The resulting algorithms were tested on two public datasets: the data recorded by the Cleveland Clinic Foundation for Heart Disease together with its extension Statlog, two of the most significant medical databases used in automated prediction. A long series of simulations were performed to assess the accuracy of the analyzed methods. In our experiments, we used F1 score and MSE (mean squared error) to compare the performance of the algorithms. The experimentally established results together with theoretical consideration prove that the proposed methods outperform both the standard ones and the considered statistical methods. We have developed improvements to the best-performing algorithms that further increase the quality of their results, being a useful tool for assisting the professionals in diagnosing CVDs in early stages.

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Section: Hybrid Methodsmentioning

confidence: 99%

Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study

et al. 2023

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“…The results demonstrated that the proposed hybrid MLP-PSO classifier empowers practitioners to diagnose cardiovascular disease earlier, with enhanced accuracy and efficacy. An approach involving a radial basis function neural network (RBFNN) was proposed in [89], which was coupled with a robust hybrid particle swarm optimization (HPSO). The HPSO incorporated a spiral-shaped mechanism (HPSO-SSM) to enhance the PSO algorithm performance by addressing constraints such as slow convergence and the local minimum challenge.…”

Section: Significance Of Feature Selection In Cardiovascular Disease ...mentioning

confidence: 99%

A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

Naser,

Majeed,

Alsabah

et al. 2024

Algorithms

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Cardiovascular disease is the leading cause of global mortality and responsible for millions of deaths annually. The mortality rate and overall consequences of cardiac disease can be reduced with early disease detection. However, conventional diagnostic methods encounter various challenges, including delayed treatment and misdiagnoses, which can impede the course of treatment and raise healthcare costs. The application of artificial intelligence (AI) techniques, especially machine learning (ML) algorithms, offers a promising pathway to address these challenges. This paper emphasizes the central role of machine learning in cardiac health and focuses on precise cardiovascular disease prediction. In particular, this paper is driven by the urgent need to fully utilize the potential of machine learning to enhance cardiovascular disease prediction. In light of the continued progress in machine learning and the growing public health implications of cardiovascular disease, this paper aims to offer a comprehensive analysis of the topic. This review paper encompasses a wide range of topics, including the types of cardiovascular disease, the significance of machine learning, feature selection, the evaluation of machine learning models, data collection & preprocessing, evaluation metrics for cardiovascular disease prediction, and the recent trends & suggestion for future works. In addition, this paper offers a holistic view of machine learning’s role in cardiovascular disease prediction and public health. We believe that our comprehensive review will contribute significantly to the existing body of knowledge in this essential area.

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“…Despite the initial success of using deep learning in clinical practice, challenges persist in recognizing multiple intersecting diseases and complex lesions, necessitating the development of additional DL-based techniques. These analytical undertakings encompass the identification of obstacles, the development of predictive models, and other vital elements that constitute the foundational components within the context of clinical intelligence-guided decisionmaking [32], [33], [34].…”

Section: Medical Research Heavily Relies On Medical Image Analysis a ...mentioning

confidence: 99%

Recent Advancements and Future Prospects in Active Deep Learning for Medical Image Segmentation and Classification

Mahmood,

Rehman,

Saba

et al. 2023

IEEE Access

Self Cite

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Medical images are helpful for the diagnosis, treatment, and evaluation of diseases. Precise medical image segmentation improves diagnosis and decision-making, aiding intelligent medical services for better disease management and recovery. Due to the unique nature of medical images, image segmentation algorithms based on deep learning face problems such as sample imbalance, edge blur, false positives, and false negatives. In view of these problems, researchers primarily improve the network structure but rarely improve from the unstructured aspect. The paper tackles these challenges, accentuating the limitations of deep convolutional neural network-based methods and proposing solutions to reduce annotation costs, particularly in complex images, and introduces the improvement strategies to solve the problems of sample imbalance, edge blur, false positives, and false negatives. Additionally, the article introduces the latest deep learning-based applications in medical image analysis, covering segmentation, image acquisition, enhancement, registration, and classification. Moreover, the article provides an overview of four cutting-edge deep learning models, namely convolutional neural network (CNN), deep belief network (DBN), stacked autoencoder (SAE), and recurrent neural network (RNN). The study selection involved searching benchmark academic databases, collecting relevant literature and appropriate indicator for analysis, emphasizing DL-based segmentation and classification approaches, and evaluating performance metrics. The research highlights clinicians' and scholars' obstacles in developing an efficient and accurate malignancy prognostic framework based on state-of-the-art deep-learning algorithms. Furthermore, future perspectives are explored to overcome challenges and advance the field of medical image analysis.

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Adaptive Hyperparameter Fine-Tuning for Boosting the Robustness and Quality of the Particle Swarm Optimization Algorithm for Non-Linear RBF Neural Network Modelling and Its Applications

Cited by 14 publications

References 39 publications

Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study

Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study

A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

Recent Advancements and Future Prospects in Active Deep Learning for Medical Image Segmentation and Classification

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