Evidence map›Paper›PMID 39620332›Full record

ArticleCurrent diabetes reviews2025

Design of a Low-Complexity Deep Learning Model for Diagnosis of Type 2 Diabetes.

Soroush Soltanizadeh, Majid Mobini, Seyedeh Somayeh Naghibi

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Article in Current diabetes reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Soroush SoltanizadehBiomedical Engineering Group Department, University of Mazandaran University of Science and Technology, Babol, Iran.ORCID 0009-0002-2377-8096
Majid MobiniFaculty Member of Department of ECE, Babol University of Technology, Babol University of Technology, Shariati Street, Babol, Iran.ORCID 0000-0003-2514-7490
Seyedeh Somayeh NaghibiBiomedical Engineering Group Department, University of Mazandaran University of Science and Technology, Babol, Iran.ORCID 0000-0003-2459-8273

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecent research demonstrates that diabetes can lead to heart problems, neurological damage, and other illnesses.

methodIn this paper, we design a low-complexity Deep Learning (DL)-based model for the diagnosis of type 2 diabetes. In our experiments, we use the publicly available PIMA Indian Diabetes Dataset (PIDD). To obtain a low-complexity and accurate DL architecture, we perform an accuracy-versus-complexity study on several DL models.

resultThe results show that the proposed DL structure, including Convolutional Neural Networks and Multi-Layer Perceptron models (i.e., CNN+MLP model) outperforms other models with an accuracy of 93.89%.

conclusionWith these features, the proposed hybrid model can be used in wearable devices and IoT-based health monitoring applications.

Indexed as

Deep LearningDiabetes Mellitus, Type 2Diagnosis, Computer-AssistedHumansNeural Networks, ComputerComplexitydeep learningdiabetesheart problemsinternet of thingsneurological damage.

Identifiers

PMID39620332

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Registered trials

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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.