Abstract
Purpose Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction. Design/methodology/approach Health professionals and stakeholders are striving for classification models to support prognosis of diabetes and formulate strategies for prevention. The authors conduct literature review of machine models and propose an intelligent framework for diabetes prediction. Findings The authors provide critical analysis of machine learning models, propose and evaluate an intelligent machine learning-based architecture for diabetes prediction. The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework. Originality/value This paper provides novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. The authors identify the training methodologies, models evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. The research results can be used by health professionals, stakeholders, students and researchers working in the diabetes prediction area.
Highlights
Machine learning modeling is an intelligent way to extract the hidden relationship among different variables in a dataset
We propose an intelligent diabetes mellitus prediction framework (IDMPF) using machine learning models, as support for allied health professionals, consisting of doctors, dieticians, medical technologists, therapists and pathologists, for better diagnosis and prognosis of diseases, for better patient care
We evaluate the models in terms of accuracy, precision, recall, F-measure, receiver operating characteristic (ROC) curve, area under the ROC curve (AUC) and execution time using a dataset having 65,839 observations
Summary
Machine learning modeling is an intelligent way to extract the hidden relationship among different variables in a dataset It has been used as a decision-support system for prediction in different applications’ domains such as healthcare, education and industry [1,2,3]. The objective of a machine learning classification model is to predict the class of a given input data [5]. They are heavily used in healthcare for disease diagnosis and prognosis, fraud detection, drug efficiency and the development of a. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/ legalcode
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