medical-7

Diabetes Prediction

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

XBNet : An Extremely Boosted Neural Network

Tushar Sarkar•Tue Jun 08 2021

A novel architecture XBNet is described, which tries to combine tree-based models with that of neural networks to create a robust architecture trained by using a novel optimization technique, Boosted Gradient Descent for Tabular Data which increases its interpretability and performance.

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Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction

Weinan Zhang, Mingcheng Chen, Zhenghui Wang, Zhiyun Zhao, Xiawei Guo, Jian Shen, Yanru Qu, Jieli Lu, Min Xu, Yu Xu, Tiange Wang, Mian Li, Weiwei Tu, Yong Yu, Y. Bi, Weiqing Wang, G. Ning•Fri Aug 13 2021

The proposed Task-wise Split Gradient Boosting Trees (TSGB) is proposed for the multi-center diabetes prediction task and reveals a problem when directly applying GBDT in MTL, i.e., the negative task gain problem.

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System

L. Ismail, Alain Hennebelle, Huned Materwala•Tue Jan 24 2023

HealthEdge is proposed, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system and shows that RF predicts diabetes with 6% more accuracy on average compared to LR.

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Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for Chronic Disease Prediction

Changlong Sun, Xiaozhong Liu, Yang Wu, Xurui Li, Xuhong Zhang, Yangyang Kang•Tue Sep 05 2023

This work proposes a novel approach for binary decision-making, which hierarchically builds community-based PU models and then aggregates their deliverables, and demonstrates the superior performance of PUtree as well as its variants on two benchmarks and a new diabetes-prediction dataset.

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Integrating Heterogeneous Gene Expression Data through Knowledge Graphs for Improving Diabetes Prediction

R. T. Sousa, Heiko Paulheim•Mon Apr 22 2024

Improvements in diabetes prediction are revealed when integrating multiple gene expression datasets and domain-specific knowledge about protein functions and interactions using knowledge graphs, a unique tool for biomedical data integration.

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