Stroke prediction using ensemble learning with clinical and image features

Abstract

A stroke is a life-threatening brain attack that disrupts blood flow into the brain. As a result, brain cells start to die due to a lack of oxygen and nutrients. After a stroke, every minute is most important. Approximately 1.9 million brain cells die per minute. Early diagnosis of stroke can save the life of a stroke patient or can reduce the permanent damage to the brain. For earlier stroke detection, an initial investigation uses the patient’s clinical information. Then, doctors advise computed tomography images of the brain. If doctors delay diagnosis or may make erroneous diagnoses, this can be a life-threatening issue. For that reason, an automatic diagnosis of stroke from clinical data initially and then finally from a brain CT scan image will be beneficial for stroke patients. For the clinical data, we have applied different machine learning models, such as Logistic Regression, Decision Tree, K-Nearest Neighbour, Ada-Boost, Xg-Boost, and others. In the case of clinical data, three balancing techniques: Random Oversampling, SMOTE, and ADASYN are employed and also record the performance of individual models. For the brain CT image data, we have moderated three pre-trained CNN models named Inceptionv3, MobileNetv2, and Xception by updating the top layer of those models using the transfer learning technique. A new ensemble convolutional neural network model named ENSNET is proposed for automatic brain stroke prediction from brain CT scan images. ENSNET is the average of two improved CNN models named Inceptionv3 and Xception. We have used accuracy, precision, recall, f1- score, confusion matrix, accuracy vs. epoch, loss vs. epoch, and ROC curve as performance evaluation matrices. The accuracy of the moderated Inceptionv3 is 97.48%, the moderated MobileNetv2 is 83.29%, and the moderated Xception is 96.11%. However, when it comes to diagnosing stroke from brain CT scans, the proposed ensemble model ENSNET outperforms the other models, offering 98.86% accuracy, 97.71% precision, 98.46% recall, 98.08% f1-score, and 98.74% AUC. This proposed ensemble model (ENSNET) is validated by using another two datasets. So, the proposed ENSNET model will be beneficial for the health sector in detecting stroke from the brain-computed tomography images of the brain more successfully than other models.