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July 30, 2024Advances in Multidisciplinary & Scientific Research Journal Publication

An Evaluation of Machine Learning Algorithms for an Enhanced Precision Healthcare in Stroke Prediction

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Authors

FAFatimah Adamu-FikaDADeborah Ifeoluwa AyekuTSTsentob Joy Samson

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Overview

Analysis reveals random forest outperforms other models in stroke prediction accuracy, suggesting class balance enhances outcomes.

Key Points

  • Random Forest achieved the highest accuracy of 98% and an AUC of 0.98, making it a leading method in stroke prediction.
  • While SVM showed a competitive accuracy of 96%, Logistic Regression displayed limitations with an 88% recall.
  • The study employed machine learning algorithms to address class imbalance using techniques like SMOTE for better predictions.
  • Findings illustrate the critical role of model evaluation and balancing in developing effective predictive healthcare tools.

Cite This Study

Adamu-Fika et al. (2024) studied this question.

synapsesocial.com/papers/68af7f467567bf4f94ff63e7https://doi.org/10.22624/aims/accrabespoke2024p32
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine Learning Techniques for Predicting Brain Stroke Risk: Addressing Data Imbalance2025
  2. 2Evaluating machine learning models for stroke prediction based on clinical variables2025 · 22 citations
  3. 3Machine learning for stroke prediction using imbalanced data2025 · 9 citations
  4. 4Predicting Stroke Risk Based on an Optimized Machine Learning Model2025
  5. 5Enhancing Brain Stroke Prediction Using Machine Learning for Early Intervention2025 · 2 citations