Performance Evaluation of Machine Learning and Deep Learning Models for Predicting Type-2 Diabetes on Balanced and Imbalanced Data

CCDS Authors

Ashraful Islam, PhD

Center DirectorDirector, Human-Computer Interaction Wing

References

1. 1.Ahmedbahaaaldin Ibrahem Ahmed Osman, Ali Najah Ahmed, Chow Ming Fai, Yuk Feng Huang, Ahmed El‐Shafie. (2021). Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia. Ain Shams Engineering Journal, 12(2), 1545–1556[10.1016/j.asej.2020.11.011]
2. 2.Aishwarya Madhav Mujumdar, V. Vaidehi. (2019). Diabetes Prediction using Machine Learning Algorithms. Procedia Computer Science, 165, 292–299[10.1016/j.procs.2020.01.047]
3. 3.Victor Chang, Meghana Ashok Ganatra, Karl Hall, Lewis Golightly, Qianwen Xu. (2022). An assessment of machine learning models and algorithms for early prediction and diagnosis of diabetes using health indicators. Healthcare Analytics, 2, 100118[10.1016/j.health.2022.100118]
4. 4.Xuchun Wang, Jiahui Ren, Hao Ren, Wenzhu Song, Yuchao Qiao, Ying Zhao, Liqin Linghu, Yu Cui, Zhiyang Zhao, Limin Chen, Lixia Qiu. (2023). Diabetes mellitus early warning and factor analysis using ensemble Bayesian networks with SMOTE-ENN and Boruta. Scientific Reports, 13(1), 12718[10.1038/s41598-023-40036-5]
5. 5.Ahmed I. B. ElSeddawy, Faten Khalid Karim, Aisha Mohamed Hussein, Doaa Sami Khafaga. (2022). Predictive Analysis of Diabetes-Risk with Class Imbalance. Computational Intelligence and Neuroscience, 2022, 1–16[10.1155/2022/3078025]
6. 6.Edward Yellakuor Baagyere, Agebure Apambila Moses, Qin Zhen, Oyetunji Elkanah Olaosebikan, Zhiguang Qin. (2020). A Multi-Spiking Neural Network Learning Model for Data Classification. IEEE Access, 8, 72360–72371[10.1109/access.2020.2985257]
7. 7.(2021). Towards a Smart Healthcare System. International Journal of Healthcare Information Systems and Informatics, 16(4), 0[10.4018/ijhisi.20211001oa08]