Optimal Feature Selection and Prediction of Diabetes using Boruta- LASSO Techniques
- Authors: Vijayshri Nitin Khedkar1, Sonali Mahendra Kothari2, Sina Patel3, Saurabh Sathe4
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View Affiliations Hide Affiliations1 Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International [Deemed University], Maharashtra India 2 Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International [Deemed University], Maharashtra India 3 Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International [Deemed University], Maharashtra India 4 San Jose State University, California USA
- Source: Research Trends in Artificial Intelligence: Internet of Things , pp 80-95
- Publication Date: December 2023
- Language: English
Optimal Feature Selection and Prediction of Diabetes using Boruta- LASSO Techniques, Page 1 of 1
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Diabetes prediction is an ongoing research problem. The sooner diabetes is detected in a human, the sooner lives and medical resources can be saved. Predicting diabetes as early as possible with easy to measures parameters with optimal accuracy is an ongoing problem. When dealing with large data, feature selection plays an important role. It not only reduces the computational cost but also increases the performance of a model. This study ensemble three different types of feature selection techniques: filter, wrapper and embedded. Ensembling Boruta and LASSO features give optimal results. Also, effectively handling class imbalance leads to better results.
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