ArticlePloS one2015
Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review on Applications of Artificial Intelligence for Obesity Prevention.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- Harnessing Artificial Intelligence in Obesity Research and Management: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Prediction of obesity levels based on physical activity and eating habits with a machine learning model integrated with explainable artificial intelligence.Frontiers in physiology · 2025Article
- Visualization obesity risk prediction system based on machine learning.Scientific reports · 2024Article
- Machine learning allows robust classification of visceral fat in women with obesity using common laboratory metrics.Scientific reports · 2024Article
- Article
- Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies.Journal of medical Internet research · 2022Article
- Construction of Predictive Model for Type 2 Diabetic Retinopathy Based on Extreme Learning Machine.Diabetes, metabolic syndrome and obesity : targets and therapy · 2022Article
- The Prediction of Body Mass Index from Negative Affectivity through Machine Learning: A Confirmatory Study.Sensors (Basel, Switzerland) · 2021Article
- Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods.IEEE access : practical innovations, open solutions · 2021Article
- Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models.IEEE open journal of engineering in medicine and biology · 2021Article
- Machine learning and blood pressure.Journal of clinical hypertension (Greenwich, Conn.) · 2019Article
- Correlations of Complete Blood Count with Alanine and Aspartate Transaminase in Chinese Subjects and Prediction Based on Back-Propagation Artificial Neural Network (BP-ANN).Medical science monitor : international medical journal of experimental and clinical research · 2017Article
- Association of blood glucose and lipid levels with complete blood count indices to establish a regression model.Biomedical reports · 2017Article
- A new machine-learning method to prognosticate paraquat poisoned patients by combining coagulation, liver, and kidney indices.PloS one · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The number of the overweight people continues to rise across the world. Studies have shown that being overweight can increase health risks, such as high blood pressure, diabetes mellitus, coronary heart disease, and certain forms of cancer. Therefore, identifying the overweight status in people is critical to prevent and decrease health risks. This study explores a new technique that uses blood and biochemical measurements to recognize the overweight condition. A new machine learning technique, an extreme learning machine, was developed to accurately detect the overweight status from a pool of 225 overweight and 251 healthy subjects. The group included 179 males and 297 females. The detection method was rigorously evaluated against the real-life dataset for accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic (ROC) curve) criterion. Additionally, the feature selection was investigated to identify correlating factors for the overweight status. The results demonstrate that there are significant differences in blood and biochemical indexes between healthy and overweight people (p-value < 0.01). According to the feature selection, the most important correlated indexes are creatinine, hemoglobin, hematokrit, uric Acid, red blood cells, high density lipoprotein, alanine transaminase, triglyceride, and γ-glutamyl transpeptidase. These are consistent with the results of Spearman test analysis. The proposed method holds promise as a new, accurate method for identifying the overweight status in subjects.
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