Journal of Natural Science Research and Review (JNSRR)

Development of a Deep Learning Model for the Early Prediction of Stroke

Chijindu, Asogwa Tochukwu

24 October 2025 · Vol. 1, Issue 4, pp. 78-83

DOI: 10.65150/EP-jnsrr/V1E4/2025-02

Abstract

This study presents the development of a deep learning model for the prediction of stroke using a healthcare dataset. The dataset was thoroughly preprocessed, encompassing feature extraction, data partitioning, and missing value management, to ensure optimal conditions for model training and evaluation. Regularisation techniques were included into a Convolutional Neural Network (CNN) architecture that was carefully designed with layers tuned for classification tasks in order to decrease overfitting and enhance generalisation. The model's performance was comprehensively assessed using a 10-fold cross-validation technique, which produced metrics such as accuracy, precision, recall, F1-score, and the Area Under the Curve (AUC). The findings showed that the CNN's average accuracy, precision, recall, and F1-score were 94.88%, 94.28%, and 95.15%, respectively. The calculated AUC of 0.96 revealed the model's high discriminating capacity.  These results show the model's resilience and dependability across several data subsets, suggesting its potential for efficient stroke detection.

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Chijindu, & Asogwa Tochukwu (2025). Development of a Deep Learning Model for the Early Prediction of Stroke. Journal of Natural Science Research and Review, 1(4), 78-83. https://doi.org/10.65150/EP-jnsrr/V1E4/2025-02

@article{Chijindu2025,
  title   = {Development of a Deep Learning Model for the Early Prediction of Stroke},
  author  = {Chijindu and Asogwa Tochukwu},
  journal = {Journal of Natural Science Research and Review},
  year    = {2025},
  volume  = {1},
  number  = {4},
  pages   = {78-83},
  doi     = {10.65150/EP-jnsrr/V1E4/2025-02},
  url     = {https://doi.org/10.65150/EP-jnsrr/V1E4/2025-02}
}

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