[HTML][HTML] Computational modeling for medical data: From data collection to knowledge discovery
Y Yang, S Xu, Y Hong, Y Cai, W Tang, J Wang… - The Innovation …, 2024 - the-innovation.org
Biomedical data encompasses images, texts, physiological signals, and molecular omics
data. As the costs of various data acquisition methods, such as genomic sequencing …
data. As the costs of various data acquisition methods, such as genomic sequencing …
Hybrid ant lion mutated ant colony optimizer technique with particle swarm optimization for leukemia prediction using microarray gene data
Leukemia refers to a type of blood malignancy that develops due to certain hematological
disorders. Identifying leukemia at its earlier stages through clinical operations are highly …
disorders. Identifying leukemia at its earlier stages through clinical operations are highly …
Cardiac Healthcare Digital Twins Supported by Artificial Intelligence-Based Algorithms and Extended Reality—A Systematic Review
Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital
Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which …
Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which …
Intelligent decision support system in healthcare using machine learning models
A Patnaik, KP K - Recent Patents on Engineering, 2024 - benthamdirect.com
Background: The use of intelligent decision support systems (IDSS) is widespread in the
healthcare industry, particularly for real-time data, client and family history datasets, and …
healthcare industry, particularly for real-time data, client and family history datasets, and …
Detecting one-pixel attacks using variational autoencoders
J Alatalo, T Sipola, T Kokkonen - World Conference on Information …, 2022 - Springer
In the field of medical imaging, artificial intelligence solutions are used for diagnosis,
prediction and treatment processes. Such solutions are vulnerable to cyberattacks …
prediction and treatment processes. Such solutions are vulnerable to cyberattacks …
Machine Learning Algorithms for Disease Diagnosis using Medical Records: A Comparative Analysis
The development of machine learning algorithms has revolutionized the medical data
categorization industry through the introduction of artificial intelligence. The development of …
categorization industry through the introduction of artificial intelligence. The development of …
A comprehensive study on algorithms and applications of artificial intelligence in diagnosis and prognosis: AI for healthcare
Abstract Machine learning and deep learning are branches of artificial intelligence
consisting of statistical, probabilistic, and optimisation techniques that allow machines to …
consisting of statistical, probabilistic, and optimisation techniques that allow machines to …
Would You Trust an AI Doctor? Building Reliable Medical Predictions with Kernel Dropout Uncertainty
The growing capabilities of AI raise questions about their trustworthiness in healthcare,
particularly due to opaque decision-making and limited data availability. This paper …
particularly due to opaque decision-making and limited data availability. This paper …
Optimal progressive classification study using SMOTE-SVM for stages of lung disease
R Sujitha, B Paramasivan - Automatika: časopis za automatiku …, 2023 - hrcak.srce.hr
Sažetak Data used in big data applications are typically kept in decentralized computing
resources in the real world, which has an impact on the design of artificial intelligence …
resources in the real world, which has an impact on the design of artificial intelligence …
Improving medical diagnostics with machine learning: a study on data classification algorithms
This paper investigates the effectiveness of the logistic regression (LR) and random forest
(RF) algorithms for classifying breast cancer using the Breast Cancer Wisconsin Dataset …
(RF) algorithms for classifying breast cancer using the Breast Cancer Wisconsin Dataset …