A proximal policy optimisation algorithm-based algorithm for cardiovascular disorders detection

Journal of Medical Engineering & Technology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 20

Published: March 11, 2025

Cardiovascular diseases (CVDs) significantly impact athletes, impacting the heart and blood vessels. This article introduces a novel method to assess CVD in athletes through an artificial neural network (ANN). The model utilises mutual learning-based bee colony (ML-ABC) algorithm set initial weights proximal policy optimisation (PPO) address imbalanced classification. ML-ABC uses learning enhance process by updating positions of food sources with respect best fitness outcomes two randomly selected individuals. PPO makes updates ANN stable efficient improve model's reliability. Our approach formulates classification problem as series decision-making processes, rewarding every act higher rewards for correctly identifying instances minority class, hence handling class imbalance. We evaluated performance on diversified medical dataset including 26,002 who were examined within Polyclinic Occupational Health Sports Zagreb, further validated NCAA NHANES datasets verify generalisability. findings indicate that our outperforms existing models accuracies 0.88, 0.86 0.82 respective datasets. These results clinical application advance cardiovascular disorder detection methodologies.

Language: Английский

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Risk prediction of hyperuricemia based on particle swarm fusion machine learning solely dependent on routine blood tests

BMC Medical Informatics and Decision Making, Journal Year: 2025, Volume and Issue: 25(1)

Published: March 14, 2025

Hyperuricemia has seen a continuous increase in incidence and trend towards younger patients recent years, posing serious threat to human health highlighting the urgency of using technological means for disease risk prediction. Existing prediction models hyperuricemia typically include two major categories indicators: routine blood tests biochemical tests. The potential alone not yet been explored. Therefore, this paper proposes model that integrates Particle Swarm Optimization (PSO) with machine learning, which can accurately assess by relying solely on data. In addition, an interpretability method based Explainable Artificial Intelligence(XAI) is introduced help medical staff understand how makes decisions. This uses Cohen's d value compare differences indicators between non-hyperuricemia identifies factors through multivariate logistic regression. Subsequently, constructed parameter optimization five learning PSO algorithm. accuracy sensitivity proposed particle swarm fusion Stacking reach 97.8% 97.6%, marking improvement over 11% compared state-of-the-art models. Finally, analysis affecting results conducted XAI method. also developed Health Portrait Platform models, enabling real-time online assessment. Since only test data are used, new better feasibility scalability, providing valuable reference assessing occurrence.

Language: Английский

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Advanced machine learning framework for enhancing breast cancer diagnostics through transcriptomic profiling

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 17, 2025

This study proposes an advanced machine learning (ML) framework for breast cancer diagnostics by integrating transcriptomic profiling with optimized feature selection and classification techniques. A dataset of 1759 samples (987 patients, 772 healthy controls) was analyzed using Recursive Feature Elimination, Boruta, ElasticNet selection. Dimensionality reduction techniques, including Non-Negative Matrix Factorization (NMF), Autoencoders, transformer-based embeddings (BioBERT, DNABERT), were applied to enhance model interpretability. Classifiers such as XGBoost, LightGBM, ensemble voting, Multi-Layer Perceptron, Stacking trained grid search cross-validation. Model evaluation conducted accuracy, AUC, MCC, Kappa Score, ROC, PR curves, external validation performed on independent 175 samples. XGBoost LightGBM achieved the highest test accuracies (0.91 0.90) AUC values (up 0.92), particularly NMF BioBERT. The Voting method exhibited best accuracy (0.92), confirming its robustness. Transformer-based techniques significantly improved performance compared conventional approaches like PCA Decision Trees. proposed ML enhances diagnostic interpretability, demonstrating strong generalizability dataset. These findings highlight potential precision oncology personalized diagnostics.

Language: Английский

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Feature Selection in Breast Cancer Gene Expression Data Using KAO and AOA with SVM Classification

Journal of Medical Systems, Journal Year: 2025, Volume and Issue: 49(1)

Published: March 26, 2025

Language: Английский

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SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 30, 2025

In this study, we propose a novel approach for breast cancer classification that integrates the Seagull Optimization Algorithm (SGA) feature selection with Random Forest (RF) classifier effective data classification. The novelty of our lies in first-time application SGA gene diagnosis, where systematically explores space to identify most informative subsets, thereby improving accuracy and reducing computational complexity. selected features are subsequently classified using RF, known its robustness high handling complex datasets. To evaluate effectiveness proposed method, compared it other classifiers, including Linear Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN). SGA-RF combination achieved best mean 99.01% 22 genes, outperforming methods demonstrating consistent performance across varying subsets. accuracies ranged from 85.35 94.33%, highlighting balance between reduction accuracy. Future work will explore integration nature-inspired algorithms deep learning models further enhance clinical applicability.

Language: Английский

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Wavelet and AI-based reservoir evaporation modeling for optimized water management: a case study of Koudiat Acerdoun Dam

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(4)

Published: March 28, 2025

Language: Английский

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Identification of biomarkers associated with M1 macrophages in the ST-segment elevation myocardial infarction through bioinformatics and machine learning approaches

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 1, 2025

ST-segment elevation myocardial infarction (STEMI) is considered a critical cardiac condition with poor prognosis. Shortly after STEMI occurs, the increased number of circulating leukocytes including macrophages can lead to accumulation more cells in myocardium, affecting immune microenvironment. Identifying serum biomarkers associated infiltration important for diagnosing and treating STEMI. In this work, we aimed use integrated bioinformatics machine learning methods identify new biomarkers. First, candidate genes closely M1 macrophage were obtained using limma package, CIBERSORTx weighted gene coexpression network analysis (WGCNA), protein‒protein interaction (PPI) networks from GSE59867 dataset, which comprises peripheral blood mononuclear cell (PBMC) samples. The patients subsequently stratified into subtypes ConsensusClusterPlus package. Furthermore, methods, identified AKT3, GJC2, HMGCL RBM17 as greatest potential be during acute phase Finally, expression profile diagnostic value four feature validated GSE62646 datasets 24 real-time PCR. This study revealed logically comprehensively that RBM17, are derived PBMCs, could enhance accuracy diagnosis might provide effective treatment options patients.

Language: Английский

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Impact of Climate Change on River Flow, Using a Hybrid Model of LARS_WG and LSTM: A Case Study in the Kashkan Basin

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104956 - 104956

Published: April 1, 2025

Language: Английский

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Normalized mean difference (NMD): a novel filter-based feature selection method

International Journal of Machine Learning and Cybernetics, Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Language: Английский

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Enhanced Crested Ibis Algorithm: Performance Validation in Benchmark Functions, Engineering Problem, and Application in Brain Tumor Detection

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 128231 - 128231

Published: May 1, 2025

Language: Английский

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