Enhancing thyroid disease prediction and comorbidity management through advanced machine learning frameworks

Clinical eHealth, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Bio-Inspired Algorithms-Based Machine Learning and Deep Learning Models in Healthcare 6.0

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 25 - 60

Published: Jan. 10, 2025

Recent advancements in deep learning (DL) and machine (ML) have opened doors for revolutionary applications healthcare, with better patient care, diagnosis, treatment. Bio-inspired algorithms, drawing inspiration from natural processes, gained attention their potential to enhance ML DL models this field. This paper explores current research directions challenges utilizing bio-inspired algorithms advancing healthcare models. We investigate facilitating feature selection, while acknowledging limitations such as scalability, interpretability, robustness noisy data. Ethical considerations surrounding use sensitive contexts are discussed. Through interdisciplinary collaboration innovative algorithmic approaches, we strive overcome these fully unlock the of ultimately aiming revolutionize delivery improved outcomes, personalized treatment strategies, more accurate diagnoses.

Language: Английский

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An Ontological Model based on Machine Learning for Predicting Breast Cancer

International Journal of Advanced Computer Science and Applications, Journal Year: 2022, Volume and Issue: 13(7)

Published: Jan. 1, 2022

Breast cancer is mostly a female disease, but it may affect men as well even at considerably lower percentage. An automated diagnosis system should be built for early detection because manual breast takes long time. Doctors have lately achieved significant advances in the identification and treatment of order to decrease rate mortality caused by latter. Researchers, on other hand, are analysing large amounts complicated medical data employing combination statistical machine learning methodologies assist clinicians predicting cancer. Various approaches, including ontology-based Machine Learning methods, played an essential role science building that can identify This study examines evaluates most popular algorithms, besides ontological model based Learning. Among classification methods investigated were Naive Bayes, Decision Tree, Logistic Regression, Support Vector Machine, Artificial Neural Network, Random Forest, k-Nearest Neighbours. The dataset utilized has 683 instances available download from Kaggle website. findings assessed using performance measures generated confusion matrix, such F-Measure, Accuracy, Precision, Recall. ontology surpassed all techniques, according results.

Language: Английский

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A State-of-art on Damage Detection of Composite Structure Using Lamb Wave and Deep Learning Techniques

International Journal of Precision Engineering and Manufacturing-Smart Technology, Journal Year: 2025, Volume and Issue: 3(1), P. 65 - 82

Published: Jan. 1, 2025

Composite materials have garnered the attention of high-tech companies due to their significant benefits, such as high stiffness, corrosion resistance, good wear and mechanical strength. However, these often suffer from damage heterogeneous nature, inability identify recognize flaws in early stages can lead catastrophic failures. By applying non-destructive testing evaluation, be detected characterized, potentially reducing unplanned breakdowns repair costs. This review examines current findings on interlaminar intralaminar detection localization using Lamb waves recent deep learning techniques. Additionally, this work addresses existing research gaps highlights potential opportunities for future improvements field structural health monitoring.

Language: Английский

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Hybrid EEG-fNIRS Detection of MCI Subtypes Based on Transformer Network

SN Computer Science, Journal Year: 2025, Volume and Issue: 6(4)

Published: March 20, 2025

Language: Английский

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