A Novel Extra Tree Ensemble Optimized DL Framework (ETEODL) for Early Detection of Diabetes

Frontiers in Public Health

Monika Arya¹  Atef Zaguia²  Hanumat Sastry G³  Sunil Kumar³  Anand Motwani⁴ 

[1] Department of Computer Science and Engineering, Bhilai Institute of Technology, Durg, India;Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia;School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India;School of Computing Science and Engineering, VIT Bhopal University, Sehore, India;

关键词: data stream classification;    deep learning;    diabetes detection;    ensemble technique;    extra tree ensemble;    machine learning;   

DOI  :  10.3389/fpubh.2021.797877

来源: DOAJ

【 摘 要 】

Diabetes has been recognized as a global medical problem for more than half a century. Patients with diabetes can benefit from the Internet of Things (IoT) devices such as continuous glucose monitoring (CGM), intelligent pens, and similar devices. Smart devices generate continuous data streams that must be processed in real-time to benefit the users. The amount of medical data collected is vast and heterogeneous since it is gathered from various sources. An accurate diagnosis can be achieved through a variety of scientific and medical techniques. It is necessary to process this streaming data faster to obtain relevant and significant knowledge. Recently, the research has concentrated on improving the prediction model's performance by using ensemble-based and Deep Learning (DL) approaches. However, the performance of the DL model can degrade due to overfitting. This paper proposes the Extra-Tree Ensemble feature selection technique to reduce the input feature space with DL (ETEODL), a predictive framework to predict the likelihood of diabetes. In the proposed work, dropout layers follow the hidden layers of the DL model to prevent overfitting. This research utilized a dataset from the UCI Machine learning (ML) repository for an Early-stage prediction of diabetes. The proposed scheme results have been compared with state-of-the-art ML algorithms, and the comparison validates the effectiveness of the predictive framework. This proposed work, which outperforms the other selected classifiers, achieves a 97.38 per cent accuracy rate. F1-Score, precision, and recall percent are 96, 97.7, and 97.7, respectively. The comparison unveils the superiority of the suggested approach. Thus, the proposed method effectively improves the performance against the earlier ML techniques and recent DL approaches and avoids overfitting.

【 授权许可】

Unknown