An Empirical Model to Predict the Diabetic Positive Using Stacked Ensemble Approach
Bibliographic Data
| ID | 22071429 |
|---|---|
| Authors | Sivashankari R, R Sivashankari (Vellore Institute of Technology University), M Sudha (0009-0000-0343-4451, Vellore Institute of Technology University), Sudha M, Mohammad Kamrul Hasan (0000-0002-8565-9511, National University of Malaysia, corresponding author), Rashid A Saeed (0000-0002-9872-081X, Taif University), Suliman A Alsuhibany (0000-0001-7735-9781, Qassim University), Sayed Abdel-Khalek, S Abdel‐Khalek (0000-0003-1822-2456, Taif University) |
| Year | 2022 |
| Volume | 9 |
| Pages | 792124-792124 |
| Publication date | 2022-01-21 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2021.792124 |
| PMID | 35127623 |
| OpenAlex | W4206943792 |
| Language | EN |
| References cited | 39 |
Today, disease detection automation is widespread in healthcare systems. The diabetic disease is a significant problem that has spread widely all over the world. It is a genetic disease that causes trouble for human life throughout the lifespan. Every year the number of people with diabetes rises by millions, and this affects children too. The disease identification involves manual checking so far, and automation is a current trend in the medical field. Existing methods use a single algorithm for the prediction of diabetes. For complex problems, a single model is not enough because it may not be suitable for the input data or the parameters used in the approach. To solve complex problems, multiple algorithms are used. These multiple algorithms follow a homogeneous model or heterogeneous model. The homogeneous model means the same algorithm, but the model has been used multiple times. In the heterogeneous model, different algorithms are used. This paper adopts a heterogeneous ensemble model called the stacked ensemble model to predict whether a person has diabetes positively or negatively. This stacked ensemble model is advantageous in the prediction. Compared to other existing models such as logistic regression Naïve Bayes (72), (74.4), and LDA (81%), the proposed stacked ensemble model has achieved 93.1% accuracy in predicting blood sugar disease
Automation · Bayes' theorem · Bayesian probability · Data mining · Ensemble forecasting · Ensemble learning · Genetic algorithm · Logistic regression · Machine learning · Naive Bayes classifier · Support vector machine · Artificial Intelligence in Healthcare · Computer Science · COVID-19 diagnosis using AI · Engineering · Machine Learning in Healthcare · Artificial Intelligence
| Highly cited | No |
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