International Journal of Electronics and Communications System (Dec 2021)
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.