Key result
The review concluded that combining multiple machine learning classifiers, such as SVM and artificial neural networks with feature selection techniques like PCA, yields the highest accuracy for early diabetes prediction.
Why the study?
Multiple diabetes prediction models existed, prompting a need to find, critically assess, and combine data from pertinent, high-quality individual research.
This review assesses multiple machine learning models for diabetes prediction to identify the best strategies for synthesizing high-quality research.
Answers are grounded in this paper and cite their sources.
Should not change diabetes screening practice; leaves open need for prospective validation of ensemble ML models.
Prediction of diabetes using machine learning algorithms has been thoroughly studied by several researchers in the past. Finding, critically assessing, and combining the data of all pertinent, high-quality individual research is what prompted us to conduct this assessment of multiple diabetes prediction models. Analysis of several writers' expertise on diabetes prediction systems is presented in this publication. This study on diabetes prediction models aimed to discover the best strategies for selecting and synthesising the many studies of high quality. The majority of medical data is nonlinear, correlation-structured, and complicated, making it difficult to analyse. The use of machine learning-based techniques in healthcare and medical imagery has been ruled out.
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Anushka Awasthi (2022) conducted a review in Diabetes. Machine learning algorithms was evaluated on Classification accuracy. The review concluded that combining multiple machine learning classifiers, such as SVM and artificial neural networks with feature selection techniques like PCA, yields the highest accuracy for early diabetes prediction.
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