A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques.

Autor: Krishnamoorthi R; Department of ECE, Vignan's Institute of Management and Technology for Women, Kondapur (V), Ghatkesar (M), Medchal-Malkajgiri (D), Padamatisaiguda, Telangana 501301, India., Joshi S; Computer Engineering, SVKM'S NMIMS, MPSTME Shirpur Campus, Savalade, India., Almarzouki HZ; Department of Radiology, Faculty of Medicine, King Abdulaziz University Hospital, Jeddah, Saudi Arabia., Shukla PK; Computer Science & Engineering Department, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (Technological University of Madhya Pradesh), Bhopal 462033, India., Rizwan A; Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia., Kalpana C; Dept. of CSE SST College of Art and Commerce, Ulhasnagar, India., Tiwari B; Ethiopia Hawassa University, Awasa, Ethiopia.
Jazyk: angličtina
Zdroj: Journal of healthcare engineering [J Healthc Eng] 2022 Jan 11; Vol. 2022, pp. 1684017. Date of Electronic Publication: 2022 Jan 11 (Print Publication: 2022).
DOI: 10.1155/2022/1684017
Abstrakt: Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population's health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study's primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.
Competing Interests: The authors of this manuscript declare that they do not have any conflicts of interest.
(Copyright © 2022 Raja Krishnamoorthi et al.)
Databáze: MEDLINE
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