Diabetes mellitus prediction using machine learning within the scope of a generic framework
- Title
- Diabetes mellitus prediction using machine learning within the scope of a generic framework
- Creator
- Arora N.; Srivastava S.; Agarwal R.; Mehndiratta V.; Tripathi A.
- Description
- Artificial intelligence (AI) based automated disease prediction has recently taken a significant place in the field of health informatics. However, due to unavailability of real time large scale medical data, the dynamic learning of prediction models remains principally subsided. This paper, therefore proposes a dynamic predictive modelling framework for chronic diseases prediction in real-time. The framework premise suggests creation of a centralized patient-indexed medical database to dynamically train machine learning (ML) models and predict risk levels of chronic diseases in real time. In this study, comprehensive empirical evaluations to train seven state-of-the-art ML models for diabetes risk prediction are performed in context of phase 2 of the suggested framework. The selected optimal model can then be dynamically applied to predict diabetes in phase 3 of the framework. Various metrics such as accuracy, precision, Recall, F1-score and receiver operating characteristic (ROC) curve are employed for evaluating performances of the trained models. Parameter tunings using different type of kernels, different number of neighbors and estimators are rigorously performed in order to create a suggestive literature for healthcare prediction ecosystem. Comparative analysis indicates high prediction accuracies on diabetes test data records for neural network and support vector machine (SVM) models as compared to other applied models. 2023 Institute of Advanced Engineering and Science. All rights reserved.
- Source
- Indonesian Journal of Electrical Engineering and Computer Science, Vol-32, No. 3, pp. 1724-1735.
- Date
- 2023-01-01
- Publisher
- Institute of Advanced Engineering and Science
- Subject
- Chronic diseases; Classification; Diabetes; Machine learning; Predictive modelling
- Coverage
- Arora N., Department of Computer Science, Kalindi College, University of Delhi, Delhi, India; Srivastava S., School of Sciences, Christ (Deemed to be University), Delhi NCR, Ghaziabad, India; Agarwal R., Department of Information Technology, Raj Kumar Goel Institute of Technology, Ghaziabad, India; Mehndiratta V., School of Sciences, Christ (Deemed to be University), Delhi NCR, Ghaziabad, India; Tripathi A., Department of Data Science and Engineering, Manipal University Jaipur, Jaipur, India
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 25024752
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Arora N.; Srivastava S.; Agarwal R.; Mehndiratta V.; Tripathi A., “Diabetes mellitus prediction using machine learning within the scope of a generic framework,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/14488.