A Novel Wrapper-Based Feature Selection for Heart Failure Prediction Using an Adaptive Particle Swarm Grey Wolf Optimization

Le, Tuan Minh; Pham, Tan Nhat; Dao, Son Vu Truong

doi:10.1007/978-3-030-70111-6_15

Cited by 10 publications

(3 citation statements)

References 22 publications

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“…• Better metaheuristic algorithms: PSO is currently used to improve the feasible solution over time, as shown in the previous section. However, PSO is known to suffer from local traps and premature convergence [32,33]. It is possible to hybridize PSO with other local-search techniques, as shown in [19,20] to improve the exploitation and local trap escaping capabilities.…”

Section: Discussionmentioning

confidence: 99%

Intermediary RRT*-PSO: A Multi-Directional Hybrid Fast Convergence Sampling-Based Path Planning Algorithm

Q. Huynh,

V. Tran,

N. K. Phan

et al. 2023

Computers, Materials &Amp; Continua

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Path planning is a prevalent process that helps mobile robots find the most efficient pathway from the starting position to the goal position to avoid collisions with obstacles. In this paper, we propose a novel path planning algorithm-Intermediary RRT * -PSO-by utilizing the exploring speed advantages of Rapidly exploring Random Trees and using its solution to feed to a metaheuristic-based optimizer, Particle swarm optimization (PSO), for fine-tuning and enhancement. In Phase 1, the start and goal trees are initialized at the starting and goal positions, respectively, and the intermediary tree is initialized at a random unexplored region of the search space. The trees were grown until one met the other and then merged and re-initialized in other unexplored regions. If the start and goal trees merge, the first solution is found and passed through a minimization process to reduce unnecessary nodes. Phase 2 begins by feeding the minimized solution from Phase 1 as the global best particle of PSO to optimize the path. After simulating two special benchmark configurations and six practice configurations with special cases, the results of the study concluded that the proposed method is capable of handling small to large, simple to complex continuous environments, whereas it was very tedious for the previous method to achieve.

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Section: Discussionmentioning

confidence: 99%

Intermediary RRT*-PSO: A Multi-Directional Hybrid Fast Convergence Sampling-Based Path Planning Algorithm

Q. Huynh,

V. Tran,

N. K. Phan

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Generally, a supervised model can either be a filter or wrapper method. An additional approach deriving from the two previous ones was called a hybrid method [ 23 , 24 , 25 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Machine Learning in Epileptic Seizure Detection

Tran

et al. 2022

Diagnostics

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Epileptic seizure is a neurological condition caused by short and unexpectedly occurring electrical disruptions in the brain. It is estimated that roughly 60 million individuals worldwide have had an epileptic seizure. Experiencing an epileptic seizure can have serious consequences for the patient. Automatic seizure detection on electroencephalogram (EEG) recordings is essential due to the irregular and unpredictable nature of seizures. By thoroughly analyzing EEG records, neurophysiologists can discover important information and patterns, and proper and timely treatments can be provided for the patients. This research presents a novel machine learning-based approach for detecting epileptic seizures in EEG signals. A public EEG dataset from the University of Bonn was used to validate the approach. Meaningful statistical features were extracted from the original data using discrete wavelet transform analysis, then the relevant features were selected using feature selection based on the binary particle swarm optimizer. This facilitated the reduction of 75% data dimensionality and 47% computational time, which eventually sped up the classification process. After having been selected, relevant features were used to train different machine learning models, then hyperparameter optimization was utilized to further enhance the models’ performance. The results achieved up to 98.4% accuracy and showed that the proposed method was very effective and practical in detecting seizure presence in EEG signals. In clinical applications, this method could help relieve the suffering of epilepsy patients and alleviate the workload of neurologists.

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“…ML has contributed to the field of healthcare by detecting multiple diseases and enabling specialists to achieve great results. Supervised learning is commonly used in classification problems by training data using machine algorithms with the corresponding outputs [ 8 , 9 , 10 , 11 ]. Research papers for PD are proposed to give timely and advanced recognition by using Machine Learning (ML) based on vocal signals, along with many researchers’ works to enhance prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Vocal Features for Parkinson’s Disease Classification Using Evolutionary Algorithms

Dao

Tran

et al. 2022

Diagnostics

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Parkinson’s Disease (PD) is a brain disorder that causes uncontrollable movements. According to estimation, roughly ten million individuals worldwide have had or are developing PD. This disorder can have severe consequences that affect the patient’s daily life. Therefore, several previous works have worked on PD detection. Automatic Parkinson’s Disease detection in voice recordings can be an innovation compared to other costly methods of ruling out examinations since the nature of this disease is unpredictable and non-curable. Analyzing the collected vocal records will detect essential patterns, and timely recommendations on appropriate treatments will be extremely helpful. This research proposed a machine learning-based approach for classifying healthy people from people with the disease utilizing Grey Wolf Optimization (GWO) for feature selection, along with Light Gradient Boosted Machine (LGBM) to optimize the model performance. The proposed method shows highly competitive results and has the ability to be developed further and implemented in a real-world setting.

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A Novel Wrapper-Based Feature Selection for Heart Failure Prediction Using an Adaptive Particle Swarm Grey Wolf Optimization

Cited by 10 publications

References 22 publications

Intermediary RRT*-PSO: A Multi-Directional Hybrid Fast Convergence Sampling-Based Path Planning Algorithm

Intermediary RRT*-PSO: A Multi-Directional Hybrid Fast Convergence Sampling-Based Path Planning Algorithm

Application of Machine Learning in Epileptic Seizure Detection

An Analysis of Vocal Features for Parkinson’s Disease Classification Using Evolutionary Algorithms

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