ArticleThe 7th International Conference on Wireless, Intelligent and Distributed Environment for Communication : WIDECOM 2024. International Conference on Wireless, Intelligent and Distributed Environment for Communication (7th : 2024 : Keene,...2025
Survey-Based Machine Learning Models for Early Detection of Diabetes.
Article in The 7th International Conference on Wireless, Intelligent and Distributed Environment for Communication : WIDECOM 2024. International Conference on Wireless, Intelligent and Distributed Environment for Communication (7th : 2024 : Keene,..., 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Diabetes, a severe and chronic condition characterized by elevated blood glucose levels, has been a significant health challenge. In recent years, Machine learning has shown promise in predicting the identification of diabetes and its related complications. However, the development of these predictive models has been hindered by inconsistencies, poor data quality, inappropriate correlational models, and the inherent complexity of clinical data. These issues often render existing machine learning algorithms for diagnosis and treatment ineffective. In this paper, we propose a novel survey-based machine-learning model for the early detection of diabetes. This model, which has the potential to enhance the effectiveness of existing machine learning algorithms significantly, utilizes the synthetic minority oversampling technique and machine learning techniques, thereby advancing healthcare technology and aiding healthcare professionals in diabetes diagnosis and creating effective prevention strategies.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.