Optimized stacking ensemble models for the prediction of diabetic progression

Author:

V. K. DaliyaORCID,Ramesh T. K.

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Reference34 articles.

1. Abdollahi J, Nouri-Moghaddam B (2021) Hybrid stacked ensemble combined with genetic algorithms for Prediction of Diabetes arXiv:2103.08186

2. Akula R, Ni N, Garibay I (2017) Supervised machine learning based ensemble model for accurate prediction of type 2 diabetes, for disease control national diabetes statistics report

3. Alama F, Mehmoodb R, Katiba I, Albeshria A (2016) Analysis of eight data mining algorithms for smarter internet of things (IoT). In: International workshop on data mining in IoT systems, DaMIS

4. Alehegn M, Joshi RR, Mulay P (2019) Diabetes analysis and prediction using random forest, KNN, Naïve Bayes, and J48: an ensemble approach. Int J Sci Technol Res 8(09). (issn 2277-8616 1346, ijstr)

5. Ang Q, Liu Z, Wang W, Li K, Chen W-K (2010) Explored research on data preprocessing and mining technology for clinical data. 2nd IEEE international conference on information management and engineering

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