A Comparative Analysis for Stroke Risk Prediction Using Machine Learning Algorithms and Convolutional Neural Network Model

Abstract

A critical, sometimes fatal medical disease called a stroke happens when the blood flow to a portion of the brain is broken off. In the case of stroke, urgent treatment is very essential. Nowadays, stroke is the main cause of death and impairment globally, according to WHO. In this situation, it will be very helpful if we predict the probability of stroke earlier depending on some most important features. Many researchers use different machine learning algorithms for prediction but very few researchers use stacking methods and CNN. The main contribution of this paper is to develop a stacking classifier of ensemble methods and the CNN model. In this paper, data-set is collected from Kaggle. Stroke data is imbalanced. Random oversampling is used for balancing data-set. Then most important features are find out using feature selection method, then applying different machine learning algorithms such as Logistic Regression, Decision Tree Classifier, Support vector machine, Random forest classifier, KNearest neighbour's classifier, Bernoulli Naïve Bayes, Gaussian Naïve Bayes, Stacking of six algorithms (Decision Tree Classifier, Support vector machine, Random forest classifier, KNearest neighbor's classifier, Bernoulli Naïve Bayes, Gaussian Naïve Bayes) and CNN. Then comparing the performances for predicting the probability of stroke during both the training and testing periods. Results show that the Stacking of six algorithms gives the highest accuracy, which is 99.89% for testing and 100% for training.