# An Integrative Paradigm for Enhanced Stroke Prediction: Synergizing XGBoost and xDeepFM Algorithms

arXiv: https://arxiv.org/abs/2310.16430 · HTML: https://mlsanity.com/paper/2310.16430/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018)
- [BotTriNet: A Unified and Efficient Embedding for Social Bots Detection via Metric Learning](https://mlsanity.com/paper/2304.03144/) (2023)
- [BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns](https://mlsanity.com/paper/2303.10214/) (2023)
- [FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction](https://mlsanity.com/paper/2304.11794/) (2023)
- [MedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regression](https://mlsanity.com/paper/2305.11742/) (2023)
- [FakeSwarm: Improving Fake News Detection with Swarming Characteristics](https://mlsanity.com/paper/2305.19194/) (2023)

## Built on by (most cited first, 5 of 5)

- [Survival Prediction Across Diverse Cancer Types Using Neural Networks](https://mlsanity.com/paper/2404.08713/) (2024)
- [A Multimodal Fusion Network For Student Emotion Recognition Based on Transformer and Tensor Product](https://mlsanity.com/paper/2403.08511/) (2024)
- [A Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Model](https://mlsanity.com/paper/2407.08942/) (2024)
- [Curriculum Recommendations Using Transformer Base Model with InfoNCE Loss And Language Switching Method](https://mlsanity.com/paper/2401.09699/) (2024)
- [Multi-modal clothing recommendation model based on large model and VAE enhancement](https://mlsanity.com/paper/2410.02219/) (2024)

## Abstract (arXiv)

Stroke prediction plays a crucial role in preventing and managing this debilitating condition. In this study, we address the challenge of stroke prediction using a comprehensive dataset, and propose an ensemble model that combines the power of XGBoost and xDeepFM algorithms. Our work aims to improve upon existing stroke prediction models by achieving higher accuracy and robustness. Through rigorous experimentation, we validate the effectiveness of our ensemble model using the AUC metric. Through comparing our findings with those of other models in the field, we gain valuable insights into the merits and drawbacks of various approaches. This, in turn, contributes significantly to the progress of machine learning and deep learning techniques specifically in the domain of stroke prediction.