Open Access

Comparative Study of Machine Learning Models for Stroke Risk Prediction

https://doi.org/10.55640/irjmshc-v03i08-05

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Dr. Pradeep Laxkar ¹ ,

⁴ Associate Professor, Department of Computer Science and Engineering, ITM (SLS) University, Vadodara, Gujarat, India

Abstract

A stroke is a chronic condition that occurs more and more every year and can be caused by the sudden stop of blood from reaching a part of the brain, causing serious neurological damage and a higher death rate if not caught on time. Early symptom detection offers insights that are important for stroke risk prediction and prompt clinical response for better stroke outcomes. This research proposes an effective machine learning-based framework for stroke risk prediction using the Kaggle Stroke Prediction Dataset. The approach involves thorough data preprocessing, such as filling missing values, label encoding, Min–Max normalization, and SMOTE–ENN data balancing, followed by the application of Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers. The experimental results demonstrate exceptional predictive performance, where the proposed RF model achieved 99.99% accuracy, 99.97% precision, 99.98% recall, and 99.96% F1-score, while the proposed XGBoost model attained 99.92% accuracy, 99.91% precision, 99.90% recall, and 99.93% F1-score. These findings indicate that ensemble machine learning techniques significantly improve stroke risk prediction.

Keywords

Stroke risk prediction, Machine learning, Healthcare decision support

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Comparative Study of Machine Learning Models for Stroke Risk Prediction

Abstract

Keywords

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