ArticleHealth information science and systems2020
Classification and prediction of diabetes disease using machine learning paradigm.
Article in Health information science and systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed, 350 citations in OpenAlex.
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- A machine learning approach for type 2 diabetes diagnosis and prognosis using tailored heterogeneous feature subsets.Medical & biological engineering & computing · 2025Article
- Symptoms affecting the development of diabetes: analysis of risk factors with data mining.BMC medical informatics and decision making · 2025Article
- Global Research Trends in Artificial Intelligence and Type 2 Diabetes Mellitus: A Bibliometric Perspective.Cureus · 2025Review
- The study of an integrated diabetes prediction model based on user-defined risk decision making strategy.Medicine · 2025Article
- Data-driven diabetes mellitus prediction and management: a comparative evaluation of decision tree classifier and artificial neural network models along with statistical analysis.Scientific reports · 2025Article
- Artificial intelligence survival models for identifying relevant risk factors for incident diabetes in Azar cohort population.Health promotion perspectives · 2025Article
- Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification.Scientific reports · 2025Article
- A novel RFE-GRU model for diabetes classification using PIMA Indian dataset.Scientific reports · 2025Article
- Leveraging a hybrid convolutional gated recursive diabetes prediction and severity grading model through a mobile app.PeerJ. Computer science · 2025Article
- Machine learning and spatio-temporal analysis of meteorological factors on waterborne diseases in Bangladesh.PLoS neglected tropical diseases · 2025Article
- A stacked ensemble machine learning approach for the prediction of diabetes.Journal of diabetes and metabolic disorders · 2024Article
- Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis.Journal of diabetes science and technology · 2024Article
- Hybrid feature selection and classification technique for early prediction and severity of diabetes type 2.PloS one · 2024Review
- An intelligent diabetes classification and perception framework based on ensemble and deep learning method.PeerJ. Computer science · 2024Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesDiabetes is a chronic disease characterized by high blood sugar. It may cause many complicated disease like stroke, kidney failure, heart attack, etc. About 422 million people were affected by diabetes disease in worldwide in 2014. The figure will be reached 642 million in 2040. The main objective of this study is to develop a machine learning (ML)-based system for predicting diabetic patients. MATERIALS AND
methodsLogistic regression (LR) is used to identify the risk factors for diabetes disease based on p value and odds ratio (OR). We have adopted four classifiers like naïve Bayes (NB), decision tree (DT), Adaboost (AB), and random forest (RF) to predict the diabetic patients. Three types of partition protocols (K2, K5, and K10) have also adopted and repeated these protocols into 20 trails. Performances of these classifiers are evaluated using accuracy (ACC) and area under the curve (AUC).
resultsWe have used diabetes dataset, conducted in 2009-2012, derived from the National Health and Nutrition Examination Survey. The dataset consists of 6561 respondents with 657 diabetic and 5904 controls. LR model demonstrates that 7 factors out of 14 as age, education, BMI, systolic BP, diastolic BP, direct cholesterol, and total cholesterol are the risk factors for diabetes. The overall ACC of ML-based system is
conclusionThe combination of LR and RF-based classifier performs better. This combination will be very helpful for predicting diabetic patients.
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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 OpenQuestion graph.