An Intelligent Ensemble Deep Learning Techniques with Improved Owl Search Algorithm-Aided Optimal Feature Selection for Predicting the Presence of Heart Diseases

Cardiovascular disease evaluation and prediction are critical medical duties to ensure precise classification, which allows heart specialists to offer treatment to patients. Heart illness is one of the deadliest diseases, particularly a sudden cardiac arrest, which strikes an individual so suddenly that there is no opportunity to manage it, and this condition is extremely hard to detect. Numerous medical machine learning, as well as data mining methods, have been employed to extract significant information about coronary artery disease prognosis. However, the precision of the intended outcomes is not adequate. An effective deep learning-assisted heart disease prediction model is presented to solve the above difficulties. At first, the data are accumulated from a standard dataset. After this process, the features from the data are optimally selected using the Improved Owl Search Algorithm (IOSA). These features are further transferred to the prediction phase. Here, the heart disease is predicted by the Ensemble Deep Attention with Convolution Network (EDACNet), and this model is composed by the combination of “Long Short-Term Memory (LSTM)”, “Gated Recurrent Unit (GRU)”, “Deep Markov Random Field (Deep MRF)” and “1Dimensional Convolutional Neural Network (1DCNN)”. The prediction scores obtained from each structure are multiplied by an optimized weight, and it is subjected to a weighted averaging process. Here, the weight is optimized by the same IOSA. The final results after the weighted averaging process provide the final predicted outcomes over heart diseases. At last, the validation process is carried out to measure the usefulness of a designed prediction model. The result of the developed model achieves better performance in terms of accuracy and precision. It shows the value of accuracy to be 96 and also the precision attained the value of 97. Therefore, the entire validation developed a model that provides effective performance over existing techniques.