# Enhancing Stroke Diagnosis in the Brain Using a Weighted Deep Learning Approach

arXiv: https://arxiv.org/abs/2504.13974 · HTML: https://mlsanity.com/paper/2504.13974/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Natural Language Processing: State of The Art, Current Trends and Challenges](https://mlsanity.com/paper/1708.05148/) (2017)
- [Attention in Natural Language Processing](https://mlsanity.com/paper/1902.02181/) (2019)
- [Music Mood Detection Based On Audio And Lyrics With Deep Neural Net](https://mlsanity.com/paper/1809.07276/) (2018)
- [Deep Cross-Modal Correlation Learning for Audio and Lyrics in Music Retrieval](https://mlsanity.com/paper/1711.08976/) (2017)
- [Exploiting Synchronized Lyrics And Vocal Features For Music Emotion Detection](https://mlsanity.com/paper/1901.04831/) (2019)
- [Using Machine Learning Based Models for Personality Recognition](https://mlsanity.com/paper/2201.06248/) (2022)

## Abstract (arXiv)

A brain stroke occurs when blood flow to a part of the brain is disrupted, leading to cell death. Traditional stroke diagnosis methods, such as CT scans and MRIs, are costly and time-consuming. This study proposes a weighted voting ensemble (WVE) machine learning model that combines predictions from classifiers like random forest, Deep Learning, and histogram-based gradient boosting to predict strokes more effectively. The model achieved 94.91% accuracy on a private dataset, enabling early risk assessment and prevention. Future research could explore optimization techniques to further enhance accuracy.