Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension

Yu, Xiaoming; Qin, Wenxiang; Lin, Xiao; Shan, Zhuohan; Huang, Liyao; Shao, Qike; Wang, Liangxing; Chen, Mayun

doi:10.1016/j.compbiomed.2023.107408

Cited by 42 publications

(6 citation statements)

References 57 publications

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“…Each of these methods possesses its unique characteristics, advantages, and applications. For example, the study of Yu et al [41] presented a hybrid model called bERIME_FKNN, which combines the enhanced rime algorithm (ERIME) and fuzzy K-Nearest Neighbor (FKNN) technique. The model uses a triangular game search strategy and a random follower search strategy to enhance global exploration.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

Alsmadi

2024

CMC

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Lung cancer is among the most frequent cancers in the world, with over one million deaths per year. Classification is required for lung cancer diagnosis and therapy to be effective, accurate, and reliable. Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner. Machine Learning (ML) has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique. Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification. Normally, microarrays include several genes and may cause confusion or false prediction. Therefore, the Arithmetic Optimization Algorithm (AOA) is used to identify the optimal gene subset to reduce the number of selected genes. Which can allow the classifiers to yield the best performance for lung cancer classification. In addition, we proposed a modified version of AOA which can work effectively on the high dimensional dataset. In the modified AOA, the features are ranked by their weights and are used to initialize the AOA population. The exploitation process of AOA is then enhanced by developing a local search algorithm based on two neighborhood strategies. Finally, the efficiency of the proposed methods was evaluated on gene expression datasets related to Lung cancer using stratified 4-fold cross-validation. The method's efficacy in selecting the optimal gene subset is underscored by its ability to maintain feature proportions between 10% to 25%. Moreover, the approach significantly enhances lung cancer prediction accuracy. For instance, Lung_Harvard1 achieved an accuracy of 97.5%, Lung_Harvard2 and Lung_Michigan datasets both achieved 100%, Lung_Adenocarcinoma obtained an accuracy of 88.2%, and Lung_Ontario achieved an accuracy of 87.5%. In conclusion, the results indicate the potential promise of the proposed modified AOA approach in classifying microarray cancer data.

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

Alsmadi

2024

CMC

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“…Many studies have focused on the effects of AI on pulmonary hypertension, from the prediction of this rare pathology in adults [ 147 , 150 , 155 , 161 , 162 ] or children [ 151 , 163 , 165 ] to the prediction of survival [ 154 , 167 ] or risk in patients with pulmonary hypertension [ 156 ], diagnosis of pulmonary hypertension [ 149 , 152 , 153 , 156 , 157 , 158 , 160 , 169 ], and the treatment of this disease [ 148 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Stamate,

Piraianu,

Ciobotaru

et al. 2024

Diagnostics

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Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

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“…It analyzes important performance measures on benchmark datasets, including precision, accuracy, area under the curve, sensitivity, and dice score; it ends with research challenges for the future. Yu presents a hybrid model, bERIME_FKNN, for early recognition and timely treatment of Pulmonary Hypertension (PH) [39]. Emam proposes an optimized residual learning architecture for classifying multiple brain tumors, utilizing an improved variant of the Hunger Games Search algorithm (I-HGS) [40].…”

Section: Literature Reviewmentioning

confidence: 99%

Advanced RIME architecture for global optimization and feature selection

Abu Khurma,

Braik,

Alzaqebah

et al. 2024

J Big Data

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The article introduces an innovative approach to global optimization and feature selection (FS) using the RIME algorithm, inspired by RIME-ice formation. The RIME algorithm employs a soft-RIME search strategy and a hard-RIME puncture mechanism, along with an improved positive greedy selection mechanism, to resist getting trapped in local optima and enhance its overall search capabilities. The article also introduces Binary modified RIME (mRIME), a binary adaptation of the RIME algorithm to address the unique challenges posed by FS problems, which typically involve binary search spaces. Four different types of transfer functions (TFs) were selected for FS issues, and their efficacy was investigated for global optimization using CEC2011 and CEC2017 and FS tasks related to disease diagnosis. The results of the proposed mRIME were tested on ten reliable optimization algorithms. The advanced RIME architecture demonstrated superior performance in global optimization and FS tasks, providing an effective solution to complex optimization problems in various domains.

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Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension

Cited by 42 publications

References 57 publications

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Advanced RIME architecture for global optimization and feature selection

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