If the prediction of disease is possible at an early stage, then the risk factors associated with diabetes can be considerably lower in severity. The main problem and highly challenging task are to predict diabetes accurately, and the reason of this challenge is the diabetes dataset's ...
In NHANES, an XGBoost classifier based on 123 variables showed an AUROC of 0.86 in detecting the presence of an established diabetes diagnosis, though the performance dropped to 0.73 when detecting undiagnosed diabetes adjudicated based on abnormal laboratory findings [50]. In a retrospective analysis ...
In the present study, physicians showed very low diagnostic accuracy compared to our AI models. Practically, physicians’ diagnosis of a specific disease is made based on the symptom of the patient, physical examination, variable hospital data including laboratory test, image data (computed tomography...
Tongue diagnosis of Traditional Chinese Medicine (TCM) is of great significance in the diagnosis of diabetes. To reduce the subjectivity of doctors in clinical diagnosis, this paper proposes a method for diabetic tongue image recognition based on machine learning. Specifically, the proposed method firs...
which improve the likelihood of prompt diagnosis. We sought to develop and validate a machine learning model based on administrative medical claims data in the electronic health record (EHR), with the goal of creating a resource to facilitate the systematic screening and identification of patients wi...
Research group stated that “Because our machine learning model included social determinants of health that are known to contribute to diabetes risk, our population-wide approach to risk assessment may represent a tool for addressing health disparities.” ...
a robust machine learning framework for diabetes prediction [Paper] impact-learning: a robust machine learning algorithm [Paper] regularization helps with mitigating poisoning attacks: distributionally-robust machine learning using the wasserstein distance [Paper] robust machine learning for colorectal ...
Ensemble learning: a rule-based decision unit was constructed using the rules in Table 2, assigning a probability of having diabetes 1 if the conditions of the first rule apply, 0 if the conditions of the second rule apply, and 0.5 to all other cases, treated as intermediate cases. This ...
Machine learning techniques trained on historical patient records have demonstrated considerable potential to predict critical events in different medical domains (for example, circulatory failure, diabetes and cardiovascular disorders)11,12,13,14,15. In the mental health domain, prediction algorithms have ...
a robust machine learning framework for diabetes prediction [Paper] impact-learning: a robust machine learning algorithm [Paper] regularization helps with mitigating poisoning attacks: distributionally-robust machine learning using the wasserstein distance [Paper] robust machine learning for colorectal ...