RETRACTED ARTICLE: An IoT based predictive modeling for Glaucoma detection in optical coherence tomography images using hybrid genetic algorithm

Singh, Law Kumar; Pooja, .; Garg, Hitendra; Khanna, Munish

doi:10.1007/s11042-022-13540-5

Cited by 9 publications

(7 citation statements)

References 64 publications

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“…Consequently, it is imperative to investigate whether biomarkers related to the shape of the FAZ possess diagnostic capabilities in individuals with AD. Recently, not only have there been reports of studies using ophthalmic imaging and AI for the diagnosis of ophthalmic diseases, but there have also been reports on their use for diagnosing AD 29 , 51 – 53 . In this study, we first revealed that multiple radiomic FAZ features, including roundness, eccentricity, compactness, and solidity, can improve the AD diagnostic performance compared with the FAZ area alone.…”

Section: Resultsmentioning

confidence: 99%

Enhancing foveal avascular zone analysis for Alzheimer’s diagnosis with AI segmentation and machine learning using multiple radiomic features

Yoon,

Lim,

Noh

et al. 2024

Sci Rep

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We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machine learning classification using multiple features extracted from the foveal avascular zone (FAZ)—a retinal biomarker for Alzheimer’s disease—to improve the disease diagnostic performance. Imaging data of optical coherence tomography angiography from 37 patients with Alzheimer’s disease and 48 healthy controls were investigated. The presence or absence of brain amyloids was confirmed using amyloid positron emission tomography. In the superficial capillary plexus of the angiography scans, the FAZ was automatically segmented using an AI method to extract multiple biomarkers (area, solidity, compactness, roundness, and eccentricity), which were paired with clinical data (age and sex) as common correction variables. We used a light-gradient boosting machine (a light-gradient boosting machine is a machine learning algorithm based on trees utilizing gradient boosting) to diagnose Alzheimer’s disease by integrating the corresponding multiple radiomic biomarkers. Fivefold cross-validation was applied for analysis, and the diagnostic performance for Alzheimer’s disease was determined by the area under the curve. The proposed hybrid technique achieved an area under the curve of $$72.2\pm 4.2$$ 72.2 ± 4.2 %, outperforming the existing single-feature (area) criteria by over 13%. Furthermore, in the holdout test set, the proposed technique exhibited a 14% improvement compared to single features, achieving an area under the curve of 72.0± 4.8%. Based on these facts, we have demonstrated the effectiveness of our technology in achieving significant performance improvements in FAZ-based Alzheimer’s diagnosis research through the use of multiple radiomic biomarkers (area, solidity, compactness, roundness, and eccentricity).

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Section: Resultsmentioning

confidence: 99%

Enhancing foveal avascular zone analysis for Alzheimer’s diagnosis with AI segmentation and machine learning using multiple radiomic features

Yoon,

Lim,

Noh

et al. 2024

Sci Rep

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“…Quantitatively evaluating the impact of nature‐based algorithms on IoT‐based healthcare services involves assessing various performance metrics to gauge their effectiveness 75 . Key quantitative measures include accuracy, precision, recall, and F1‐score for predictive analytics and diagnostic accuracy 76 . Additionally, assessing the execution time, computational efficiency, and resource utilization provides insights into the algorithms' real‐time processing capabilities, scalability, and energy efficiency.…”

Section: Results and Comparisonmentioning

confidence: 99%

“…75 Key quantitative measures include accuracy, precision, recall, and F1-score for predictive analytics and diagnostic accuracy. 76 Additionally, assessing the execution time, computational efficiency, and resource utilization provides insights into the algorithms' real-time processing capabilities, scalability, and energy efficiency. The impact can be further quantified by measuring the reduction in false positives or false negatives, demonstrating the algorithms' ability to enhance diagnostic precision and reduce medical errors.…”

Section: F I G U R Ementioning

confidence: 99%

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

Amiri,

Heidari,

Zavvar

et al. 2024

Trans Emerging Tel Tech

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Nature‐inspired algorithms revolve around the intersection of nature‐inspired algorithms and the IoT within the healthcare domain. This domain addresses the emerging trends and potential synergies between nature‐inspired computational approaches and IoT technologies for advancing healthcare services. Our research aims to fill gaps in addressing algorithmic integration challenges, real‐world implementation issues, and the efficacy of nature‐inspired algorithms in IoT‐based healthcare. We provide insights into the practical aspects and limitations of such applications through a systematic literature review. Specifically, we address the need for a comprehensive understanding of the applications of nature‐inspired algorithms in IoT‐based healthcare, identifying gaps such as the lack of standardized evaluation metrics and studies on integration challenges and security considerations. By bridging these gaps, our paper offers insights and directions for future research in this domain, exploring the diverse landscape of nature‐inspired algorithms in healthcare. Our chosen methodology is a Systematic Literature Review (SLR) to investigate related papers rigorously. Categorizing these algorithms into groups such as genetic algorithms, particle swarm optimization, cuckoo algorithms, ant colony optimization, other approaches, and hybrid methods, we employ meticulous classification based on critical criteria. MATLAB emerges as the predominant programming language, constituting 37.9% of cases, showcasing a prevalent choice among researchers. Our evaluation emphasizes adaptability as the paramount parameter, accounting for 18.4% of considerations. By shedding light on attributes, limitations, and potential directions for future research and development, this review aims to contribute to a comprehensive understanding of nature‐inspired algorithms in the dynamic landscape of IoT‐based healthcare services.

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“…This section reviews papers that have used ML approaches including SVM-based models to address several real-world case studies, ranging from the prediction of chronic diseases such as diabetes [105][106][107] and sleep apnea [108,109] to the diagnosis of glaucoma [110] and acute myocardial infarction [111]. The selected papers consider that the increasing prevalence of chronic diseases such as Type 2 diabetes mellitus places a heavy burden on healthcare systems.…”

Section: Classification and Prediction Problems In Real-world Case St...mentioning

confidence: 99%

An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review

Guido,

Ferrisi,

Lofaro

et al. 2024

Information

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Support vector machines (SVMs) are well-known machine learning algorithms for classification and regression applications. In the healthcare domain, they have been used for a variety of tasks including diagnosis, prognosis, and prediction of disease outcomes. This review is an extensive survey on the current state-of-the-art of SVMs developed and applied in the medical field over the years. Many variants of SVM-based approaches have been developed to enhance their generalisation capabilities. We illustrate the most interesting SVM-based models that have been developed and applied in healthcare to improve performance metrics on benchmark datasets, including hybrid classification methods that combine, for instance, optimization algorithms with SVMs. We even report interesting results found in medical applications related to real-world data. Several issues around SVMs, such as selection of hyperparameters and learning from data of questionable quality, are discussed as well. The several variants developed and introduced over the years could be useful in designing new methods to improve performance in critical fields such as healthcare, where accuracy, specificity, and other metrics are crucial. Finally, current research trends and future directions are underlined.

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RETRACTED ARTICLE: An IoT based predictive modeling for Glaucoma detection in optical coherence tomography images using hybrid genetic algorithm

Cited by 9 publications

References 64 publications

Enhancing foveal avascular zone analysis for Alzheimer’s diagnosis with AI segmentation and machine learning using multiple radiomic features

Enhancing foveal avascular zone analysis for Alzheimer’s diagnosis with AI segmentation and machine learning using multiple radiomic features

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review

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