Efficient approach for mining high-utility patterns on incremental databases with dynamic profits

S Kim, H Kim, M Cho, H Kim, B Vo, JCW Lin… - Knowledge-Based …, 2023 - Elsevier
High-utility itemset mining (HUIM) is one of the heavily studied fields of data mining, which is
due to its high compatibility with real-world applications. HUIM is a process of extracting a …

Risk of papillary thyroid carcinoma and nodular goiter associated with exposure to semi-volatile organic compounds: A multi-pollutant assessment based on machine …

F Wang, Y Lin, J Xu, F Wei, S Huang, S Wen… - Science of The Total …, 2024 - Elsevier
Background Exposure to semi-volatile organic compounds (SVOCs) may link to thyroid
nodule risk, but studies of mixed-SVOCs exposure effects are lacking. Traditional analytical …

[HTML][HTML] A machine learning tool for identifying patients with newly diagnosed diabetes in primary care

P Wändell, AC Carlsson, M Wierzbicka… - Primary Care …, 2024 - Elsevier
Background and aim It is crucial to identify a diabetes diagnosis early. Create a predictive
model utilizing machine learning (ML) to identify new cases of diabetes in primary health …

[HTML][HTML] Predictive modeling for the development of diabetes mellitus using key factors in various machine learning approaches

M Tanaka, Y Akiyama, K Mori, I Hosaka, K Kato… - Diabetes Epidemiology …, 2024 - Elsevier
Aims Machine learning (ML) approaches are beneficial when automatic identification of
relevant features among numerous candidates is desired. We investigated the predictive …

Machine Learning in Diabetes Modeling

S Ardabili, A Mosavi, I Felde - 2023 IEEE 21st Jubilee …, 2023 - ieeexplore.ieee.org
Machine learning (ML) has become an integral component of diabetes research. Its
applications encompass prediction, identification, classification, and diagnosis of diabetes …

Towards explainable machine learning for prediction of disease progression

S Berendse, J Krabbe, J Klaus… - Applied Artificial …, 2024 - Taylor & Francis
This research focuses on addressing the challenges surrounding interpretability of machine
learning techniques in the field of prediction of disease progression. This paper summarizes …

[PDF][PDF] Diabetes Epidemiology and Management

M Tanaka, Y Akiyama, K Mori, I Hosaka, K Kato… - 2023 - researchgate.net
Aims: Machine learning (ML) approaches are beneficial when automatic identification of
relevant features among numerous candidates is desired. We investigated the predictive …