An improved hawks optimizer based learning algorithms for cardiovascular disease prediction. (March 2023) - Kumar, A. Saran; Rekha, R.
Highlights: A novel sample-based Neural Network Reasoning model is used for classification purposes. Model's overall performance is optimized with the Hawks Optimizer (HO) to acquire the optimal and global solution. Performance metrics like Mathews Correlation Coefficient (MCC), accuracy, precision and F-measure is used for comparison with existing systems. Overall performance of proposed model is 96% accuracy, 97% precision, 95% sensitivity, 95% F-measure, 93% MCC, 3.10% FNR and 96% TPR respectively. Abstract: Earlier Cardiovascular Disease (CVD) prediction is difficult and the prediction complexity is higher due to the lack of intelligent models. This research proposes a stacked ensemble model for feature extraction/selection and classification process. A novel sample-based Neural Network Reasoning model is employed for classification purposes and the construction of the stacking model. The model's overall performance is optimized using Hawks Optimizer (HO) to acquire optimal and global solution. The dataset for predicting the CVD is obtained from the Kaggle dataset for cardiovascular disease prediction, an online resource. The class imbalance issues encountered in the dataset are also considered as an essential factor that needs to resolve for enhancing the prediction quality. Here, the simulation is performed using MATLAB 2020a and the experimental outcomes show the significance of the model. Some performance metrics like Mathews Correlation Coefficient (MCC), accuracy, precision, F-measure, etc. The comparison is made with various existing benchmark datasets. The anticipated model provides better trade-off in contrast to the existing approaches.