ArticleComputational intelligence and neuroscience2022
IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 5 papers, 1 of them a synthesis that pooled it.
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- A Systematic Review on Food Recommender Systems for Diabetic Patients.International journal of environmental research and public health · 2023Pooled it
- Clinical Decision Support System for Diabetic Patients by Predicting Type 2 Diabetes Using Machine Learning Algorithms.Journal of healthcare engineering · 2023Article
- Retracted: IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction.Computational intelligence and neuroscience · 2023Article
- Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress.Computational intelligence and neuroscience · 2022Review
- Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis.Computational intelligence and neuroscience · 2022Article
Corrections and comments
- Retraction · 2023-08-02Concerns/Issues about Data · Concerns/Issues about Human Subject Welfare · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Lack of IRB/IACUC Approval and/or Compliance · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/5D89FCB44482BE7628792C14992467
- Retracted
Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, there is a growing need for Internet of Things (IoT)-based mobile healthcare applications that help to predict diseases. In recent years, several people have been diagnosed with diabetes, and according to World Health Organization (WHO), diabetes affects 346 million individuals worldwide. Therefore, we propose a noninvasive self-care system based on the IoT and machine learning (ML) that analyses blood sugar and other key indicators to predict diabetes early. The main purpose of this work is to develop enhanced diabetes management applications which help in patient monitoring and technology-assisted decision-making. The proposed hybrid ensemble ML model predicts diabetes mellitus by combining both bagging and boosting methods. An online IoT-based application and offline questionnaire with 15 questions about health, family history, and lifestyle were used to recruit a total of 10221 people for the study. For both datasets, the experimental findings suggest that our proposed model outperforms state-of-the-art techniques.
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.