Prediction of Diabetes Complications Using Computational Intelligence Techniques
Open Access
- 27 February 2023
- journal article
- research article
- Published by MDPI AG in Applied Sciences
- Vol. 13 (5), 3030
- https://doi.org/10.3390/app13053030
Abstract
Diabetes is a complex disease that can lead to serious health complications if left unmanaged. Early detection and treatment of diabetes is crucial, and data analysis and predictive techniques can play a significant role. Data mining techniques, such as classification and prediction models, can be used to analyse various aspects of data related to diabetes, and extract useful information for early detection and prediction of the disease. XGBoost classifier is a machine learning algorithm that effectively predicts diabetes with high accuracy. This algorithm uses a gradient-boosting framework and can handle large and complex datasets with high-dimensional features. However, it is important to note that the choice of the best algorithm for predicting diabetes may depend on the specific characteristics of the data and the research question being addressed. In addition to predicting diabetes, data analysis and predictive techniques can also be used to identify risk factors for diabetes and its complications, monitor disease progression, and evaluate the effectiveness of treatments. These techniques can provide valuable insights into the underlying mechanisms of the disease and help healthcare providers make informed decisions about patient care. Data analysis and predictive techniques have the potential to significantly improve the early detection and management of diabetes, a fast-growing chronic disease that notable health hazards. The XGBoost classifier showed the most effectiveness, with an accuracy rate of 89%.Keywords
This publication has 28 references indexed in Scilit:
- Hierarchical convolutional neural networks for fashion image classificationExpert Systems with Applications, 2018
- Density and velocity determination for single-phase flow based on radiotracer technique and neural networksFlow Measurement and Instrumentation, 2018
- Prediction of Diabetes Using Data Mining TechniquesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- Early detection of diabetic retinopathySurvey of Ophthalmology, 2018
- Detection and Prediction of Diabetes Mellitus Using Back-Propagation Neural NetworkPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- DB 2020: Analyzing and Forecasting Design-Build Market TrendsJournal of Construction Engineering and Management, 2016
- Performance Analysis of Data Mining Classification Techniques to Predict DiabetesProcedia Computer Science, 2016
- Prediction and diagnosis of diabetes mellitus — A machine learning approachPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal featuresJournal of Neuroscience Methods, 2010
- Prognostics in switching systems: Evidential markovian classification of real-time neuro-fuzzy predictionsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010