… relevant features from NHANES or other large data sets with rich clinical and behavioral
health data, as well as powerful ML approaches like feature selection 39 and deep learning 40 , …

… relevant features from NHANES or other large data sets with rich clinical and behavioral
health data, as well as powerful ML approaches like feature selection(39) and deep learning(40)…

… diabetes, hypertension and bladder cancer are still controversial, limited study used machine
learning … This study aimed to explore the association between diabetes, hypertension and …

… This study used a deep learning algorithm to identify depression in veterans and its
factors … [11] showed a higher risk of mortality in heart disease, diabetes, hypertension, and …

… of our findings utilizing the NHANES dataset. This section also addresses the limitations
of our scientific diabetes application and suggests potential directions for future research. …

… We were then left with 157 of 2864 variables in NHANES and 314 of 652 variables in K-NHANES
(excluding the sample index numbers and the depression outcome variables) were …

… K-NHANES), a nationally representative survey for the entire South Korean population, were
used to train deep learning and conventional machine learning … variable to deep learning, it …

… years, machine and deep learning techniques have been used to predict diabetes and its …
still face two main challenges when building type 2 diabetes predictive models. First, there is …

… Deep learning, a subset of machine learning, can learn limited … Deep learning has
shown improved data processing … Deep learning algorithms include a deep neural network (…

… Previous attempts to predict diabetes with machine learning models using NHANES data,
put forth a list of important variables [12, 13]. In the work done by Yu et al. [13], the authors …