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Predictive modelling and identification of key risk factors for stroke using machine learning.

Ahmad Hassan¹, Saima Gulzar Ahmad¹, Ehsan Ullah Munir¹

¹Department of Computer Science, COMSATS University Islamabad, Wah Campus, Grand Trunk Road, Wah, 47010, Pakistan.

Scientific Reports

|May 20, 2024

Summary

This study developed a Dense Stacking Ensemble (DSE) model for accurate stroke prediction, achieving over 96% accuracy. The DSE model effectively handles imbalanced and missing data, improving early stroke detection and patient outcomes.

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Area of Science:

Cardiovascular Medicine
Biostatistics
Machine Learning in Healthcare

Background:

Stroke is a primary cause of global mortality, necessitating improved early detection and prevention.
Accurate stroke prediction is hindered by imbalanced and missing data, complicating risk factor identification.

Purpose of the Study:

To develop and evaluate advanced machine learning models for stroke risk prediction.
To address challenges posed by imbalanced and missing data in stroke prediction models.

Main Methods:

Imputation techniques were used for missing data, and Synthetic Minority Oversampling Technique (SMOTE) for data imbalance.
A range of advanced models were evaluated using k-fold cross-validation on diverse datasets.
A Dense Stacking Ensemble (DSE) model was developed, utilizing fine-tuned advanced models.

Main Results:

Key predictors for stroke include age, BMI, glucose levels, heart disease, hypertension, and marital status.
The DSE model achieved over 96% accuracy on various datasets.
AUC scores reached 83.94% on imbalanced imputed data and 98.92% on balanced data.

Conclusions:

The Dense Stacking Ensemble (DSE) model demonstrates superior performance for stroke prediction compared to previous research.
The DSE model shows significant potential for enhancing early stroke detection and improving patient outcomes.