Support Vector Machines: A Literature Review on Their Application in Analyzing Mass Data for Public Health

Cureus, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 8, 2025

This study considers the literature on support vector machines (SVMs) in area of public health data analysis, particularly evaluating their ability to harness big for disease classification and predictions. SVMs have been remarkably embraced two decades clinical diagnosis, patient management, prediction trends owing high precision robustness. review suggests method spatially relevant system responses through assessment SVM advantages disadvantages future research agendas, including improving scalability, integrating with emerging sources like Internet Things (IoT) genomic data, enhancing model transparency real-world decision-making.

Language: Английский

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Overview of AI technologies and their impact on healthcare

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 45 - 71

Published: Jan. 1, 2025

Language: Английский

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Optimal Tree Depth in Decision Tree Classifiers for Predicting Heart Failure Mortality

Healthcraft Frontiers, Journal Year: 2023, Volume and Issue: 1(1), P. 58 - 66

Published: Dec. 30, 2023

The depth of a decision tree (DT) affects the performance DT classifier in predicting mortality caused by heart failure (HF). A deeper learns complex patterns within data, theoretically leading to better predictive performance. very deep also leads overfitting, because model training data rather than generalize new and unseen resulting lower classification on test data. Similarly, shallow does not learn much complexity underfitting pruning method has been proposed set limit maximum or minimum number instances required split node reduce computational complexity. Pruning helps avoid overfitting. However, it help find optimal tree. To build better-performing classifier, is crucial achieve This study cross-validation using validation In method, cross-validated accuracy for empirically tested HF dataset, which contains 299 observations with 11 features collected from Kaggle machine learning (ML) repository. observed result reveals that tuning significantly important balance process relevant are captured overfitting avoided. Although techniques prove be effective determining depth, this compare different methods determine such as grid search, algorithms, information criteria. limitation study.

Language: Английский

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Precision prevention in occupational health: a conceptual analysis and development of a unified understanding and an integrative framework

Frontiers in Public Health, Journal Year: 2024, Volume and Issue: 12

Published: Sept. 18, 2024

Introduction Precision prevention implements highly precise, tailored health interventions for individuals by directly addressing personal and environmental determinants of health. However, precision does not yet appear to be fully established in occupational There are numerous understandings conceptual approaches, but these have been systematically presented or synthesized. Therefore, this analysis aims propose a unified understanding develop an integrative framework Methods Firstly, present definitions frameworks health, six international databases were searched studies published between January 2010 2024 that used the term its synonyms context Secondly, qualitative content was conducted analyze existing understanding. Thirdly, based on identified frameworks, multi-stage exploratory development process applied Results After screening 3,681 articles, 154 publications reviewed, wherein 29 64 different found, which can summarized eight higher-order categories. The revealed seven themes illustrated many wordings. proposed takes up themes. It includes, among other things, contrast “one-size-fits-all approach” with risk- resource-oriented data collection innovative analytics profiling provide improve interventions. developed comprises three overarching stages: (1) generation, (2) management lifecycle (3) (development, implementation adaptation). Discussion Although there already offers, first time, proposal framework. should only seen as initial critically discussed further expand strengthen both research practical application workplace.

Language: Английский

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Transforming Industrial Supervision Systems: A Comprehensive Approach Integrating Machine Learning Techniques and Fuzzy Logic

˜The œScientific Bulletin of Electrical Engineering Faculty, Journal Year: 2024, Volume and Issue: 24(2), P. 52 - 66

Published: Dec. 1, 2024

Abstract In addressing the mounting challenges of industrial supervision systems grappling with intricate processes, this study pioneers a transformative paradigm centered on SCIMAT cement factory. By seamlessly integrating Machine Learning and Fuzzy Logic, primary aim is to revolutionize real-time control systems, keen focus production. SVM integration into system, coupled connectivity Programmable Logic Controller (PLC), complemented by fuzzy controllers’ regression analysis. Rigorous testing evaluation validate proposed approach’s reliability, showcasing its effectiveness in discerning optimal system functioning. The system’s practical application within PLC environment underscores prowess issuing commands equipment, thereby enhancing operational efficiency. Going beyond conventional methodologies, our approach amalgamates classification, controllers, analysis, delivering multifaceted solution for supervision. standout achievement an classification accuracy surpassing 94% compared other classifiers. K-Nearest Neighbors (K-NN) model demonstrated rate approximately 93.83%. decision tree attained around 83.73%. logistic achieved 80.25%. These models are not only adept at distinguishing functioning from faults but also preserving linguistic language used operators. study’s novelty lies holistic offering adaptable that advances significantly reduces maintenance costs, marking substantial improvement over traditional methods. This model, validated through application, establishes new standard flexibility, cost reduction, overall productivity enhancement processes.

Language: Английский

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Bibliometric Analysis of the Role of Artificial Intelligence in Detecting Maxillofacial Fractures

Cureus, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 13, 2024

Facial bone fractures are a common occurrence in trauma cases, particularly India where road traffic accidents contribute significantly. Over the past few years, artificial intelligence (AI) has become potent instrument to help medical professionals diagnose and treat facial fractures. This study aims perform bibliometric analysis, that is, quantitative qualitative of publications focusing on role AI detecting Bibliometric analysis can be very strong measure research productivity trends within given area research. Data were drawn from Dimensions database; 58 relevant scientific articles analyzed this study. assess volume area, identifying key trends, authors, institutions, countries contributing literature. The database was used gather analyze data, shedding light impact through indicators such as h-index, citation counts, publication trends. review will depict landscape work, highlighting rising influence use for accurate diagnostics further detailing gaps potential avenues future directed toward solutions standardizing datasets clinical integration.

Language: Английский

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