Stroke Prediction Analysis using Machine Learning Classifiers and Feature Technique

Abstract

Stroke is one of the fatal brain diseases that cause death in 3 to 10 hours. However, most stroke mortality can be prevented by identifying the nature of the stroke and reacting to it promptly through smart health systems. In this paper, a machine learning model is approached for predicting the existence of stroke of a patient where the Random forest classifier outperforms the state-of-the-art models, including Logistic Regression, Decision Tree Classifier (DTC), K-NN. We conduct the experiments on datasets which has 5110 observations with 12 attributes. We also applied EDA for preprocessing and feature techniques for balancing the datasets. Finally, a cloud-based mobile app collects user data to analyze and provide the possibility of stroke for alerting the person with the accuracy of precision 96%, recall 96%, and F1-score 96%. This user-friendly system can be a lifesaver as the person gets an essential warning very easily by providing very little information from anywhere with a mobile device.