Stroke Risk Prediction with Machine Learning Techniques

Abstract

A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure and accuracy. The experiment results showed that the stacking classification outperforms the other methods, with an AUC of 98.9%, F-measure, precision and recall of 97.4% and an accuracy of 98%.

Keywords: data analysis; machine learning; risk prediction; stroke.

Figure 1

Participants distribution per age group and gender type in the balanced dataset.

Figure 2

Participants distribution per hypertension and heart disease status in the balanced dataset.

Figure 3

Participants distribution per BMI category and smoke status in the balanced dataset.

Figure 4

Participants distribution per residence and work type in the balanced dataset.

Figure 5

Machine learning models AUC and F-measure evaluation for the stroke class.

Figure 6

Machine learning models precision and recall evaluation for the stroke class.