# A predictive analytics approach for stroke prediction using machine learning and neural networks

arXiv: https://arxiv.org/abs/2203.00497 · HTML: https://mlsanity.com/paper/2203.00497/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 2 of 2)

- [Identifying Stroke Indicators Using Rough Sets](https://mlsanity.com/paper/2110.10152/) (2021)
- [Interlinking Heterogeneous Data for Smart Energy Systems](https://mlsanity.com/paper/1907.02790/) (2019)

## Built on by (most cited first, 7 of 7)

- [Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images](https://mlsanity.com/paper/2211.01885/) (2022)
- [Analyzing the impact of feature selection on the accuracy of heart disease prediction](https://mlsanity.com/paper/2206.03239/) (2022)
- [Machine Learning Driven Biomarker Selection for Medical Diagnosis](https://mlsanity.com/paper/2405.10345/) (2024)
- [Optimizing Disease Prediction with Artificial Intelligence Driven Feature Selection and Attention Networks](https://mlsanity.com/paper/2408.03151/) (2024)
- [TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow Optimization](https://mlsanity.com/paper/2503.03276/) (2025)
- [Segregation and Context Aggregation Network for Real-time Cloud Segmentation](https://mlsanity.com/paper/2504.14178/) (2025)
- [StrokeNeXt: A Siamese-encoder Approach for Brain Stroke Classification in Computed Tomography Imagery](https://mlsanity.com/paper/2602.15087/) (2026)

## Abstract (arXiv)

The negative impact of stroke in society has led to concerted efforts to improve the management and diagnosis of stroke. With an increased synergy between technology and medical diagnosis, caregivers create opportunities for better patient management by systematically mining and archiving the patients' medical records. Therefore, it is vital to study the interdependency of these risk factors in patients' health records and understand their relative contribution to stroke prediction. This paper systematically analyzes the various factors in electronic health records for effective stroke prediction. Using various statistical techniques and principal component analysis, we identify the most important factors for stroke prediction. We conclude that age, heart disease, average glucose level, and hypertension are the most important factors for detecting stroke in patients. Furthermore, a perceptron neural network using these four attributes provides the highest accuracy rate and lowest miss rate compared to using all available input features and other benchmarking algorithms. As the dataset is highly imbalanced concerning the occurrence of stroke, we report our results on a balanced dataset created via sub-sampling techniques.