Abstract
The presence of high level of sugar molecules in blood for a long period of time gives rise to chronic illness which is termed as diabetes. It severely affects the functioning of other organs in the body. A precise early predicting system can be very helpful in reducing the risk and severity associated with diabetes with significant influence on having a healthy lifestyle. This paper presents an introductory application of ensemble learning for an early diabetes prediction which employs AdaBoost algorithm with Support Vector Classifier (SVC) and Decision tree (DT) as base estimators. The performance of the model is verified through different classification metrics. This article is meant to incite energy in data scientists to implement powerful machine learning models in the field of biomedical analysis.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Falvo D, Holland BE (2017) Medical and psychosocial aspects of chronic illness and disability. Jones & Bartlett Learning
Skyler JS, Bakris GL, Bonifacio E, Darsow T, Eckel RH, Groop L et al (2017) Differentiationof diabetes by pathophysiology, natural history, and prognosis. Diabetes 66:241–255
Diwani S, Mishol S, Kayange DS, Machuve D, Sam A (2013) Overview applications of data mining in health care: the case study of Arusha region. Int J Comput Eng Res 3:73–77
Alam TM, Awan MJ (2018) Domain analysis of information extraction techniques. Int J Multidiscip Sci Eng 9:1–9
Alam TM, Khan MMA, Iqbal MA, Wahab A, Mushtaq M (2019) Cervical cancer prediction through different screening methods using data mining. Int J Adv Comput Sci Appl 10:388–396
Shanker M (1996) Using neural networks to predict the onset of diabetes mellitus. J Chem Inform Comput Sci 36:35–41
Alam TM, Iqbal MA, Ali Y, Wahab A, Ijaz S, Baig TI, Hussain A, Malik MA, Raza MM, Ibrar S, Abbas Z (2019) A model for early prediction of diabetes. Inform Med Unlocked 16:100204
Lai H, Huang H et al (2019) Predictive models for diabetes mellitus using machine learning techniques. https://doi.org/10.1186/s12902-019-0436-6
Dazzi D, Taddei F, Gavarini A, Uggeri E, Negro R, Pezzarossa A (2001) The control of blood glucose in the critical diabetic patient: a neuro-fuzzy method. J Diab Complications 15(2):80–87
Zorman M, Masuda G, Kokol P, Yamamoto R, Stiglic B (2002) Mining diabetes database with decision trees and association rules. In: 15th IEEE symposium on computer-based medical systems, pp 134–139
Azrar A, Ali Y, Awais M, Zaheer K (2018) Data mining models comparison for diabetes prediction. Int J Adv Comput Sci Appl 9
Kumari VA, Chitra R (2013) Classification of diabetes disease using support vector machine. Int J Eng Res Afr 3:1797–1801
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Patra, N., Pramanik, J., Samal, A.K., Pani, S.K. (2022). Machine Learning Application in Primitive Diabetes Prediction—A Case of Ensemble Learning. In: Mallick, P.K., Bhoi, A.K., Barsocchi, P., de Albuquerque, V.H.C. (eds) Cognitive Informatics and Soft Computing. Lecture Notes in Networks and Systems, vol 375. Springer, Singapore. https://doi.org/10.1007/978-981-16-8763-1_64
Download citation
DOI: https://doi.org/10.1007/978-981-16-8763-1_64
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8762-4
Online ISBN: 978-981-16-8763-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)