Evidence map›Paper›PMID 40456809›Full record

ArticleScientific reports2025

Data-driven diabetes mellitus prediction and management: a comparative evaluation of decision tree classifier and artificial neural network models along with statistical analysis.

Idris Zubairu Sadiq, Babangida Sanusi Katsayal, Bashiru Ibrahim, Maryam Ibrahim, Hassan Aliyu Hassan, Umar Muhammad Ghali, Abdullahi Garba Usman, Abubakar Usman, Sani Isah Abba

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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. Article
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

9 authors.

Idris Zubairu SadiqDepartment of Biochemistry, Faculty of Life Sciences, Ahmadu Bello University, Zaria, Kaduna State, Nigeria. idrisubalarabe2010@gmail.com.
Babangida Sanusi KatsayalDepartment of Biochemistry, Faculty of Life Sciences, Ahmadu Bello University, Zaria, Kaduna State, Nigeria.
Bashiru IbrahimDepartment of Biochemistry, Faculty of Life Sciences, Ahmadu Bello University, Zaria, Kaduna State, Nigeria.
Maryam IbrahimDepartment of Biochemistry and Molecular Biology, Faculty of Life Sciences, Federal University, Dutsin-Ma, Katsina State, Nigeria.
Hassan Aliyu HassanDepartment of Biochemistry, Federal University, Dutse, Jigawa State, Nigeria.
Umar Muhammad GhaliDepartment of Chemistry, Faculty of Science, Cankiri Karatekin University, 18100, Çankırı, Turkey.
Abdullahi Garba UsmanOperational Research Centre in Healthcare, Near East University, TRNC Mersin 10, 99138, Nicosia, Turkey.
Abubakar UsmanDepartment of Statistics, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria.
Sani Isah AbbaDepartment of Chemical Engineering, Prince Mohammad Bin Fahd University, 31952, Al Khobar, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetes Mellitus is a chronic metabolic disorder affecting a substantial global population leading to complications such as retinopathy, nephropathy, neuropathy, foot problems, heart attacks, and strokes if left unchecked. Prompt detection and diagnosis are crucial in managing and averting these complications. This study compares the effectiveness of a Decision Tree Classifier and an Artificial Neural Network (ANN) in predicting Diabetes Mellitus. The Decision Tree Classifier demonstrated superior performance, achieving a 97.7% accuracy rate compared to the ANN's 94.7%. The Decision Tree Classifier also achieved higher precision (96.9% vs. 88.8%) and recall (96.5% vs. 90.2%) than the ANN, along with a balanced F1 score of 96.5% versus 90.2%. The Matthews Correlation Coefficient (MCC) confirmed a stronger correlation between predictions and actual labels for the Decision Tree Classifier (87.4%) compared to the ANN (78%). Furthermore, the Area Under Curve (AUC) score of 96% for the Decision Tree Classifier was higher than that of ANN (78%). The relative importance feature analysis clearly established glycated hemoglobin (HbA1c) as the paramount factor in predicting diabetes mellitus. Diabetic patients showed markedly higher cholesterol and triglycerides, increasing cardiovascular risk, while High Density Lipoprotein (HDL) and Low-Density Lipoprotein (LDL) levels showed no significant difference between diabetics and non-diabetics. However, Very Low-Density Lipoprotein (VLDL) was significantly elevated, suggesting altered lipid transport in diabetes. Body Mass Index (BMI) was also notably higher in diabetics, reinforcing the link between obesity and diabetes risk. Principal Component analysis further highlighted five clusters of health-related variables, identifying age-related metabolic indicators (AGE, HbA1c, BMI), kidney function markers (creatinine (Cr), Urea), cardiovascular lipid profiles (Cholesterol, LDL), lipid transport (VLDL), and protective cardiovascular indicator (HDL). The study highlights the superiority of decision tree classifier in predicting Diabetes Mellitus, suggesting its potential for significant clinical applications in diagnosis and management.

Indexed as

Decision TreesDiabetes MellitusNeural Networks, ComputerAdultAgedFemaleGlycated HemoglobinHumansMaleMiddle AgedGlycated HemoglobinArtificial neural networkDecision tree classifierDiabetes mellitusMachine learningModellingPrediction

Identifiers

PMID40456809
PMCPMC12130537

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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.