ArticleBMC medical informatics and decision making2021
Exploratory study on classification of diabetes mellitus through a combined Random Forest Classifier.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 76 citations in OpenAlex.
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- Pharmacokinetic and Pharmacodynamic Drug-Drug Interactions Between Cofrogliptin and Metformin in Healthy Subjects: A Single-Center, Single-Arm, Phase I Study in China.Clinical pharmacology in drug development · 2026Article
- A machine learning-based framework for predicting type 2 diabetes mellitus using hematological indices.American heart journal plus : cardiology research and practice · 2026Article
- Prediction, lag and mixture effects of meteorology and pollutants on the incidence of pulmonary tuberculosis in Jining City, China.BMC public health · 2026Article
- Mung bean seed classification based on multimodal features and Kepler-optimized stacking ensemble learning model.PloS one · 2026Article
- Addressing data imbalance in collision risk prediction with active generative oversampling.Scientific reports · 2025Article
- Detecting B-cell lymphoma-6 overexpression status in primary central nervous system lymphoma using multiparametric MRI-based machine learning.Neuroradiology · 2025Article
- Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.BMC public health · 2024Article
- Factors affecting the survival of prediabetic patients: comparison of Cox proportional hazards model and random survival forest method.BMC medical informatics and decision making · 2024Article
- Interrelated feature selection from health surveys using domain knowledge graph.Health information science and systems · 2023Article
- DiabeticSense: A Non-Invasive, Multi-Sensor, IoT-Based Pre-Diagnostic System for Diabetes Detection Using Breath.Journal of clinical medicine · 2023Article
- Clinical Decision Support System for Diabetic Patients by Predicting Type 2 Diabetes Using Machine Learning Algorithms.Journal of healthcare engineering · 2023Article
- The PBC Model: Supporting Positive Behaviours in Smart Environments.Sensors (Basel, Switzerland) · 2022Article
- Integrated statistical and machine learning analysis provides insight into key influencing symptoms for distinguishing early-onset type 2 diabetes.Chronic diseases and translational medicine · 2022Article
- Article
- Predicting the 2-Year Risk of Progression from Prediabetes to Diabetes Using Machine Learning among Chinese Elderly Adults.Journal of personalized medicine · 2022Article
- Efficacy Analysis of Team-Based Nursing Compliance in Young and Middle-Aged Diabetes Mellitus Patients Based on Random Forest Algorithm and Logistic Regression.Computational and mathematical methods in medicine · 2022Article
- Machine Learning-Based Shear Wave Elastography Elastic Index (SWEEI) in Predicting Cervical Lymph Node Metastasis of Papillary Thyroid Microcarcinoma: A Comparative Analysis of Five Practical Prediction Models.Cancer management and research · 2022Article
- Early detection system of risk factors for diabetes mellitus type 2 utilization of machine learning-random forest.Journal of family & community medicineArticle
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiabetes Mellitus (DM) has become the third chronic non-communicable disease that hits patients after tumors, cardiovascular and cerebrovascular diseases, and has become one of the major public health problems in the world. Therefore, it is of great importance to identify individuals at high risk for DM in order to establish prevention strategies for DM.
methodsAiming at the problem of high-dimensional feature space and high feature redundancy of medical data, as well as the problem of data imbalance often faced. This study explored different supervised classifiers, combined with SVM-SMOTE and two feature dimensionality reduction methods (Logistic stepwise regression and LAASO) to classify the diabetes survey sample data with unbalanced categories and complex related factors. Analysis and discussion of the classification results of 4 supervised classifiers based on 4 data processing methods. Five indicators including Accuracy, Precision, Recall, F1-Score and AUC are selected as the key indicators to evaluate the performance of the classification model.
resultsAccording to the result, Random Forest Classifier combining SVM-SMOTE resampling technology and LASSO feature screening method (Accuracy = 0.890, Precision = 0.869, Recall = 0.919, F1-Score = 0.893, AUC = 0.948) proved the best way to tell those at high risk of DM. Besides, the combined algorithm helps enhance the classification performance for prediction of high-risk people of DM. Also, age, region, heart rate, hypertension, hyperlipidemia and BMI are the top six most critical characteristic variables affecting diabetes.
conclusionsThe Random Forest Classifier combining with SVM-SMOTE and LASSO feature reduction method perform best in identifying high-risk people of DM from individuals. And the combined method proposed in the study would be a good tool for early screening of DM.
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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.