Comparative Study on Different Machine Learning Algorithms for Neonatal Diabetes Detection

Document Type : Research Paper

Authors

1 Saveetha School of Law, Saveetha Institute of Medical and Technical Sciences, Chennai-77, India.

2 Department of Computer Science and Information Technology, IGNOU, New Delhi, India.

3 Department of Electrical Engineering, SVKMs Institute of Technology, Dhule, M.S. 424002, India.

4 CDOE, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India.

5 School of Engineering, Sree Vidyanikethan Engineering College, Andra Pradesh, India.

6 Faculty of Economics and Business, Universities Jambi, Indonesia.

10.22059/jitm.2024.96359

Abstract

This paper gives a performance analysis of multiple vote classifiers based on meta-classification methods for estimating the risk of diabetes. The study's dataset includes a number of biological and clinical risk variables that can result in the development of diabetes. In the analysis, classifiers like Random Forest, Logistic Regression, Gradient Boosting, Support Vector Machines, and Artificial Neural Networks were used. In the study, each classifier was trained and evaluated separately, and the outcomes were compared to those attained using meta-classification methods. Some of the meta-classifiers used in the analysis included Majority Voting, Weighted Majority Voting, and Stacking. The effectiveness of each classifier was evaluated using a number of measures, including accuracy, precision, recall, F1-score, and Area under the Curve (AUC). The results show that meta-classification techniques often outperform solo classifiers in terms of prediction precision. Random Forest and Gradient Boosting, two different classifiers, had the highest accuracy, while Logistic Regression performed the worst. The best performing meta-classifier was stacking, which achieved an accuracy of 84.25%. Weighted Majority Voting came in second (83.86%) and Majority Voting came in third (82.95%).

Keywords

References

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Journal of Information Technology Management
Volume 16, Issue 1
2024
Pages 5-26

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