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.

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

Ahmed, S. T. (2017, June). A study on multi objective optimal clustering techniques for medical datasets. In 2017 international conference on intelligent computing and control systems (ICICCS) (pp. 174-177). IEEE.
Ahmed, S. T., Singh, D. K., Basha, S. M., Abouel Nasr, E., Kamrani, A. K., & Aboudaif, M. K. (2021). Neural network based mental depression identification and sentiments classification technique from speech signals: A COVID-19 Focused Pandemic Study. Frontiers in public health, 9, 781827.
Ahmed, S. T., Sreedhar Kumar, S., Anusha, B., Bhumika, P., Gunashree, M., & Ishwarya, B. (2020). A generalized study on data mining and clustering algorithms. New Trends in Computational Vision and Bio-inspired Computing: Selected works presented at the ICCVBIC 2018, Coimbatore, India, 1121-1129.
Alehegn, M., Joshi, R., & Alehegn, M. (2017). Analysis and prediction of diabetes diseases using machine learning algorithm: Ensemble approach. International Research Journal of Engineering and Technology, 4(10), 426-436.
Alehegn, M., Joshi, R., & Mulay, P. (2018). Analysis and prediction of diabetes mellitus using machine learning algorithm. International Journal of Pure and Applied Mathematics, 118(9), 871-878.
Choudhary, S., Kumar, A., & Choudhary, S. (2022, September). Prediction and Comparison of Diabetes with Logistic Regression, Naïve Bayes, Random Forest, and Support Vector Machine. In International Conference on Innovations in Computer Science and Engineering (pp. 273-283). Singapore: Springer Nature Singapore.
Choudhury, A., & Gupta, D. (2019). A survey on medical diagnosis of diabetes using machine learning techniques. In Recent Developments in Machine Learning and Data Analytics: IC3 2018 (pp. 67-78). Springer Singapore.
Doğru, A., Buyrukoğlu, S., & Arı, M. (2023). A hybrid super ensemble learning model for the early-stage prediction of diabetes risk. Medical & Biological Engineering & Computing, 61(3), 785-797.
Hasan, M. K., Alam, M. A., Das, D., Hossain, E., & Hasan, M. (2020). Diabetes prediction using ensembling of different machine learning classifiers. IEEE Access, 8, 76516-76531.
Huang, Y. P., & Nashrullah, M. (2016, November). SVM-based decision tree for medical knowledge representation. In 2016 International Conference on Fuzzy Theory and Its Applications (iFuzzy) (pp. 1-6). IEEE.
Karimian, G., Petelos, E., & Evers, S. M. (2022). The ethical issues of the application of artificial intelligence in healthcare: a systematic scoping review. AI and Ethics, 2(4), 539-551.
Kee, O. T., Harun, H., Mustafa, N., Abdul Murad, N. A., Chin, S. F., Jaafar, R., & Abdullah, N. (2023). Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review. Cardiovascular Diabetology, 22(1), 13.
Khanam, J. J., & Foo, S. Y. (2021). A comparison of machine learning algorithms for diabetes prediction. Ict Express, 7(4), 432-439.
Lai, H., Huang, H., Keshavjee, K., Guergachi, A., & Gao, X. (2019). Predictive models for diabetes mellitus using machine learning techniques. BMC endocrine disorders, 19, 1-9.
Mansoori, A., Sahranavard, T., Hosseini, Z. S., Soflaei, S. S., Emrani, N., Nazar, E. & Mobarhan, M. G. (2023). Prediction of type 2 diabetes mellitus using hematological factors based on machine learning approaches: a cohort study analysis. Scientific Reports, 13(1), 663.
Meng, X. H., Huang, Y. X., Rao, D. P., Zhang, Q., & Liu, Q. (2013). Comparison of three data mining models for predicting diabetes or prediabetes by risk factors. The Kaohsiung journal of medical sciences, 29(2), 93-99.
Mujumdar, A., & Vaidehi, V. (2019). Diabetes prediction using machine learning algorithms. Procedia Computer Science, 165, 292-299.
Sandhya, G., Charan, P., Ansari, H. F., Kathiravan, M. N., Suganthi, D., & Nishant, N. (2023, July). Integrating Technology for Sustainable Agriculture: Enhancing Crop Productivity while Minimising Pesticide Usage using Image Processing & IoT. In 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 462-468). IEEE.
Sarwar, M. A., Kamal, N., Hamid, W., & Shah, M. A. (2018, September). Prediction of diabetes using machine learning algorithms in healthcare. In 2018 24th international conference on automation and computing (ICAC) (pp. 1-6). IEEE.
Shrivastava, A., Chakkaravarthy, M., & Asif Shah, M. (2022). A Novel Approach Using Learning Algorithm for Parkinson’s Disease Detection with Handwritten Sketches’. Cybernetics and Systems, 1-17.
Shrivastava, A., Chakkaravarthy, M., & Shah, M. A. (2023). A new machine learning method for predicting systolic and diastolic blood pressure using clinical characteristics. Healthcare Analytics, 4, 100219.
Shrivastava, A., Chakkaravarthy, M., & Shah, M. A. (2023). Health Monitoring based Cognitive IoT using Fast Machine Learning Technique. International Journal of Intelligent Systems and Applications in Engineering, 11(6s), 720-729.
Sinha, A., & Singh, S. (2021). Detailed analysis of medical IoT using wireless body sensor network and application of IoT in healthcare. Human Communication Technology: Internet of Robotic Things and Ubiquitous Computing, 401-434.
Sinha, A., Bhargavi, M., Singh, N. K., Garg, N., Pal, S., & Verma, A. (2022, December). Comparative Analysis of Machine Learning and Data Mining based Multi-Models for Diabetes Risk Prediction. In 2022 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE) (pp. 1-7). IEEE.
Wang, Q., Cao, W., Guo, J., Ren, J., Cheng, Y., & Davis, D. N. (2019). DMP_MI: an effective diabetes mellitus classification algorithm on imbalanced data with missing values. IEEE access, 7, 102232-102238.
Whig, P., Gupta, K., Jiwani, N., Jupalle, H., Kouser, S., & Alam, N. (2023). A novel method for diabetes classification and prediction with Pycaret. Microsystem Technologies, 1-9.
Zheng, T., Xie, W., Xu, L., He, X., Zhang, Y., You, M. & Chen, Y. (2017). A machine learning-based framework to identify type 2 diabetes through electronic health records. International journal of medical informatics, 97, 120-127.
Journal of Information Technology Management
Volume 16, Issue 1
2024
Pages 5-26

Files

Share

How to cite

Statistics