Hybrid Machine Learning Model Using Decision Tree and Support Vector Machine for Diabetes Identification
- 5 May 2021
- book chapter
- conference paper
- Published by Springer Science and Business Media LLC
Abstract
No abstract availableKeywords
This publication has 13 references indexed in Scilit:
- Use of a K-nearest neighbors model to predict the development of type 2 diabetes within 2 years in an obese, hypertensive populationMedical & Biological Engineering & Computing, 2020
- Predictive models for diabetes mellitus using machine learning techniquesBMC Endocrine Disorders, 2019
- A Comparative Objective Assessment on Mesh-Based and SVM-Based 3D Reconstruction of MRI BrainInternational Journal of Natural Computing Research, 2019
- Diabetes Prediction using Machine Learning AlgorithmsProcedia Computer Science, 2019
- Haralick Features-Based Classification of Mammograms Using SVMPublished by Springer Science and Business Media LLC ,2018
- A comprehensive exploration to the machine learning techniques for diabetes identificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- Dialect Identification Using Spectral and Prosodic Features on Single and Ensemble ClassifiersArabian Journal for Science and Engineering, 2017
- Risk prediction of type II diabetes based on random forest modelPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- Diabetes Mellitus Affected Patients Classification and Diagnosis through Machine Learning TechniquesProcedia Computer Science, 2017
- A machine learning-based framework to identify type 2 diabetes through electronic health recordsInternational Journal of Medical Informatics, 2016