Key result
Hybrid optimization algorithm with deep neural networking achieves ~97% prediction accuracy for cardiac stroke.
A novel hybrid optimization algorithm using deep neural networks achieved 97.34% accuracy in predicting cardiac events in a dataset.
Hypothesis-generating for AI stroke prediction tools; should not change practice until prospectively validated.
Heart weakness and restricted blood flow into the cavities can cause a range of strokes from mild to severe Heart strokes are primary caused due to the fat deposited on artery walls. The process reduces the intake of blood and internally causes a pseudo vacuum of air bubbles leading to a stroke which can be identified with high-end instrumentations. In this article, a detailed evaluation is processed with a Hybrid Optimization Algorithm (HOA). In the proposed technique, data are preprocessed using a label encoder and the missing values of the dataset are filled. Whale Optimization Algorithm (WOA) and Crow Search Algorithm(CSA) extract inter-connected patterns and learning features using a dedicated Deep Neural Networking (DNN) support. The proposed Hybrid Optimization Algorithm extracts features and the resultant values demonstrate a high accuracy range of 97.34%.
No takes yet. Share an insight, caveat, or question.
Al-Shammari et al. (2021) studied Cardiac stroke / Cardiovascular disease (n=270). Hybrid Optimization Algorithm (HOA) under Deep Neural Network (DNN) vs. Other machine learning algorithms (e.g., WOA, ACO, SVM, Random Forest) was evaluated on Prediction accuracy. The proposed Hybrid Optimization Algorithm using a Deep Neural Network achieved a prediction accuracy of 97.34% for cardiac stroke.
Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context: