ArticleJournal of healthcare engineering2021
Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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
26 citing papers in PubMed, 196 citations in OpenAlex.
- Article
- An Interpretable Fuzzy Distance-Based Ensemble Framework with SHAP Analysis for Clinically Transparent Prediction of Diabetes.Diagnostics (Basel, Switzerland) · 2026Article
- Enhanced diabetes prediction using pre-trained CNNs, LSTM, and conditional GAN on transformed numerical data.Scientific reports · 2026Article
- Machine learning models for risk prediction of diabetic retinopathy in Fujian eye study.Frontiers in endocrinology · 2026Article
- Article
- Evolution of diabetes prediction using the fusion of ANOVA, ADASYN technique and XGBoost based on body composition data.Journal of diabetes and metabolic disorders · 2025Article
- Application of machine learning for early detection of chronic diseases in Africa.Journal of public health research · 2025Article
- A novel RFE-GRU model for diabetes classification using PIMA Indian dataset.Scientific reports · 2025Article
- DiabetesXpertNet: An innovative attention-based CNN for accurate type 2 diabetes prediction.PloS one · 2025Article
- SLR: A Modified Logistic Regression Model with Sinkhorn Divergence for Alzheimer's Disease Classification.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- GBWOEM: A Gradient-Based Weight Optimization Model for Improved Predictive Accuracy in Healthcare.F1000Research · 2025Article
- An automated approach to predict diabetic patients using KNN imputation and effective data mining techniques.BMC medical research methodology · 2024Article
- Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter.Scientific reports · 2024Article
- Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes.Diagnostics (Basel, Switzerland) · 2024Article
- Leveraging a 7-Layer Long Short-Term Memory Model for Early Detection and Prevention of Diabetes in Oman: An Innovative Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Weighted Bayesian Belief Network for diabetics: a predictive model.Frontiers in artificial intelligence · 2024Article
- Optimizing diabetes classification with a machine learning-based framework.BMC bioinformatics · 2023Article
- Article
- Assessment of Performance, Interpretability, and Explainability in Artificial Intelligence-Based Health Technologies: What Healthcare Stakeholders Need to Know.Mayo Clinic proceedings. Digital health · 2023Review
- Using Machine Learning Algorithms to Predict Patient Portal Use Among Emergency Department Patients With Diabetes Mellitus.Journal of clinical medicine research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identified for the early and onset detection and prevention of several fatal diseases. Diabetes mellitus is an extremely life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. In this paper, a machine learning based approach has been proposed for the classification, early-stage identification, and prediction of diabetes. Furthermore, it also presents an IoT-based hypothetical diabetes monitoring system for a healthy and affected person to monitor his blood glucose (BG) level. For diabetes classification, three different classifiers have been employed, i.e., random forest (RF), multilayer perceptron (MLP), and logistic regression (LR). For predictive analysis, we have employed long short-term memory (LSTM), moving averages (MA), and linear regression (LR). For experimental evaluation, a benchmark PIMA Indian Diabetes dataset is used. During the analysis, it is observed that MLP outperforms other classifiers with 86.08% of accuracy and LSTM improves the significant prediction with 87.26% accuracy of diabetes. Moreover, a comparative analysis of the proposed approach is also performed with existing state-of-the-art techniques, demonstrating the adaptability of the proposed approach in many public healthcare applications.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.