A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification DOI Creative Commons
Abdul Qadir Khan, Guangmin Sun,

Majdi Khalid

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(5), P. e0303094 - e0303094

Published: May 20, 2024

In response to the growing number of diabetes cases worldwide, Our study addresses escalating issue diabetic eye disease (DED), a significant contributor vision loss globally, through pioneering approach. We propose novel integration Genetic Grey Wolf Optimization (G-GWO) algorithm with Fully Convolutional Encoder-Decoder Network (FCEDN), further enhanced by Kernel Extreme Learning Machine (KELM) for refined image segmentation and classification. This innovative combination leverages genetic grey wolf optimization boost FCEDN’s efficiency, enabling precise detection DED stages differentiation among types. Tested across diverse datasets, including IDRiD, DR-HAGIS, ODIR, our model showcased superior performance, achieving classification accuracies between 98.5% 98.8%, surpassing existing methods. advancement sets new standard in offers potential automating fundus analysis, reducing reliance on manual examination, improving patient care efficiency. findings are crucial enhancing diagnostic accuracy outcomes management.

Language: Английский

An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study DOI Creative Commons
Munish Khanna, Law Kumar Singh,

Kapil Shrivastava

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(5), P. e26799 - e26799

Published: Feb. 28, 2024

Computer-aided diagnosis (CAD) systems play a vital role in modern research by effectively minimizing both time and costs. These support healthcare professionals like radiologists their decision-making process efficiently detecting abnormalities as well offering accurate dependable information. heavily depend on the efficient selection of features to accurately categorize high-dimensional biological data. can subsequently assist related medical conditions. The task identifying patterns biomedical data be quite challenging due presence numerous irrelevant or redundant features. Therefore, it is crucial propose then utilize feature (FS) order eliminate these primary goal FS approaches improve accuracy classification eliminating that are less informative. phase plays critical attaining optimal results machine learning (ML)-driven CAD systems. effectiveness ML models significantly enhanced incorporating during training phase. This empirical study presents methodology for using technique. proposed approach incorporates three soft computing-based optimization algorithms, namely Teaching Learning-Based Optimization (TLBO), Elephant Herding (EHO), hybrid algorithm two. algorithms were previously employed; however, addressing issues predicting human diseases has not been investigated. following evaluation focuses categorization benign malignant tumours publicly available Wisconsin Diagnostic Breast Cancer (WDBC) benchmark dataset. five-fold cross-validation technique employed mitigate risk over-fitting. approach's proficiency determined based several metrics, including sensitivity, specificity, precision, accuracy, area under receiver-operating characteristic curve (AUC), F1-score. best value computed through suggested 97.96%. clinical decision system demonstrates highly favourable performance outcome, making valuable tool practitioners secondary opinion reducing overburden expert practitioners.

Language: Английский

Citations

23
Efficient feature selection based novel clinical decision support system for glaucoma prediction from retinal fundus images DOI
Law Kumar Singh, Munish Khanna, Hitendra Garg

et al.

Medical Engineering & Physics, Journal Year: 2023, Volume and Issue: 123, P. 104077 - 104077

Published: Dec. 7, 2023

Language: Английский

Citations

25
Improved Kepler Optimization Algorithm for enhanced feature selection in liver disease classification DOI
Essam H. Houssein, Nada Abdalkarim, Nagwan Abdel Samee

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 297, P. 111960 - 111960

Published: May 16, 2024

Language: Английский

Citations

11
Prediction of sepsis mortality in ICU patients using machine learning methods DOI Creative Commons
Jiayi Gao, Yu‐Ying Lu,

Negin Ashrafi

et al.

BMC Medical Informatics and Decision Making, Journal Year: 2024, Volume and Issue: 24(1)

Published: Aug. 16, 2024

Abstract Problem Sepsis, a life-threatening condition, accounts for the deaths of millions people worldwide. Accurate prediction sepsis outcomes is crucial effective treatment and management. Previous studies have utilized machine learning prognosis, but limitations in feature sets model interpretability. Aim This study aims to develop that enhances accuracy using reduced set features, thereby addressing previous enhancing Methods analyzes intensive care patient MIMIC-IV database, focusing on adult cases. Employing latest data extraction tools, such as Google BigQuery, following stringent selection criteria, we selected 38 features this study. also informed by comprehensive literature review clinical expertise. Data preprocessing included handling missing values, regrouping categorical variables, Synthetic Minority Over-sampling Technique (SMOTE) balance data. We evaluated several models: Decision Trees, Gradient Boosting, XGBoost, LightGBM, Multilayer Perceptrons (MLP), Support Vector Machines (SVM), Random Forest. The Sequential Halving Classification (SHAC) algorithm was used hyperparameter tuning, both train-test split cross-validation methodologies were employed performance computational efficiency. Results Forest most effective, achieving an area under receiver operating characteristic curve (AUROC) 0.94 with confidence interval ±0.01. significantly outperformed other models new benchmark literature. provided detailed insights into importance various Organ Failure Assessment (SOFA) score average urine output being highly predictive. SHAP (Shapley Additive Explanations) analysis further enhanced model’s interpretability, offering clearer understanding impacts. Conclusion demonstrates significant improvements predicting model, supported advanced techniques thorough preprocessing. Our approach key impacting mortality, making accurate interpretable. By practical utility settings, offer valuable tool healthcare professionals make data-driven decisions, ultimately aiming minimize sepsis-induced fatalities.

Language: Английский

Citations

10
An Explainable Artificial Intelligence Software System for Predicting Diabetes DOI Creative Commons
Parvathaneni Naga Srinivasu, Shakeel Ahmed, M. Hassaballah

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(16), P. e36112 - e36112

Published: Aug. 1, 2024

Implementing diabetes surveillance systems is paramount to mitigate the risk of incurring substantial medical expenses. Currently, blood glucose measured by minimally invasive methods, which involve extracting a small sample and transmitting it meter. This method deemed discomforting for individuals who are undergoing it. The present study introduces an Explainable Artificial Intelligence (XAI) system, aims create intelligible machine capable explaining expected outcomes decision models. To this end, we analyze abnormal levels utilizing Bi-directional Long Short-Term Memory (Bi-LSTM) Convolutional Neural Network (CNN). In regard, acquired through oxidase (GOD) strips placed over human body. Later, signal data converted spectrogram images, classified as low glucose, average levels. labeled images then used train individualized monitoring model. proposed XAI model track real-time uses XAI-driven architecture in its feature processing. model's effectiveness evaluated analyzing performance several evolutionary metrics confusion matrix. revealed demonstrate that effectively identifies with elevated

Language: Английский

Citations

9
A comparative study on the effectiveness of interactive versus non-interactive optic disc photograph training modules for glaucoma diagnosis among ophthalmology residents DOI Creative Commons

Thanatcha Tanjirawatana,

Kitiya Ratanawongphaibul, Anita Manassakorn

et al.

Cogent Education, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 2, 2025

Early glaucoma detection through accurate optic disc interpretation is essential but challenging for ophthalmology residents. This study evaluated the effectiveness of interactive (ITM) versus non-interactive (NITM) web-based training modules in improving skills diagnosis among Ninety-six residents from five centers Thailand were randomized into ITM or NITM groups. Both groups completed pre- and post-tests containing 30 standardized photographs used self-study with 100 images obtained CLARUS™ 500 over two months. The group received immediate feedback on their answers, while only viewed correct answers without interaction. demonstrated significant improvement scores after (P < 0.001), no difference between = 0.231). Third-year showed greater score compared to first-year 0.009). Satisfaction comparable 0.416). findings suggest that both improve residents' ability evaluate glaucomatous discs, though statistically was found approaches.

Language: Английский

Citations

1
Enhanced Polar Lights Optimization with Cryptobiosis and Differential Evolution for Global Optimization and Feature Selection DOI Creative Commons
Yang Gao, Liang Cheng

Biomimetics, Journal Year: 2025, Volume and Issue: 10(1), P. 53 - 53

Published: Jan. 14, 2025

Optimization algorithms play a crucial role in solving complex problems across various fields, including global optimization and feature selection (FS). This paper presents the enhanced polar lights with cryptobiosis differential evolution (CPLODE), novel improvement upon original (PLO) algorithm. CPLODE integrates mechanism (DE) operators to enhance PLO's search capabilities. The particle collision strategy is replaced DE's mutation crossover operators, enabling more effective exploration using dynamic rate improve convergence. Furthermore, records reuses historically successful solutions, thereby improving greedy process. experimental results on 29 CEC 2017 benchmark functions demonstrate CPLODE's superior performance compared eight classical algorithms, higher average ranks faster Moreover, achieved competitive ten real-world datasets, outperforming several well-known binary metaheuristic classification accuracy reduction. These highlight effectiveness for both selection.

Language: Английский

Citations

1
Cloud-Based Optimized Deep Learning Framework for Automated Glaucoma Detection Using Stationary Wavelet Transform and Improved Grey-Wolf-Optimization with ELM Approach DOI Creative Commons
Debendra Muduli, Syed Irfan Yaqoob, Santosh Kumar Sharma

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104682 - 104682

Published: April 1, 2025

Language: Английский

Citations

1
A novel hybridized feature selection strategy for the effective prediction of glaucoma in retinal fundus images DOI
Law Kumar Singh, Munish Khanna, Shankar Thawkar

et al.

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(15), P. 46087 - 46159

Published: Oct. 21, 2023

Language: Английский

Citations

19
A three-stage novel framework for efficient and automatic glaucoma classification from retinal fundus images DOI
Law Kumar Singh, Munish Khanna, Hitendra Garg

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: June 14, 2024

Language: Английский

Citations

8