Efficient IoT-based heart disease prediction framework with Weight Updated Trans-Bidirectional Long Short Term Memory-Gated Recurrent Unit

Article type: Research Article

Authors: Sasirekha, K.^{a; *} | Asha, D.^b | Sivaganga, P.^c | Harini, R.^d

Affiliations: [a] Computer Science and Business Systems, R.M.D Engineering College, India | [b] Information technology Loyola Institute of Technology, India | [c] Artificial Intelligence and Data Science, Vel Tech High Tech Dr. R. Rangarajan Dr. Sakunthala Engineering College, India | [d] AI&DS, VelTech Multi Tech Dr .Rangarajan Dr. Sakunthala Engineering College, India

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: The integrated system has generated numerous features for the users, like as identifying heart disease by its symptoms, forwarding the information to the doctors regarding the phase of the probability of disease as well as aiding to fix it. When an emergency situation exists, the system forwards the emergency alert to the respective doctor. Moreover, the automatic system is needed to diagnose heart disease but, the larger data is not sufficient to train the model. Thus, the Internet of Things (IoT) is employed to manage the huge amount of data. Therefore, a novel prediction of heart diseases is implemented with the aid of IoT-based deep learning approaches. Here, the collected data is collected from the three standard databases and then perform preprocessed over the gathered data. Here, the IoT assisted deep learning model is performed to predict heart related diseases accurately. Further, the acquired features of heart diseases are selected using the developed Hybrid Chameleon Electric Fish Swarm Optimization (HCEFSO) via Chameleon Swarm Algorithm (CSA) and Electric Fish Optimization (EFO). Then, the optimally selected features are fed to the training process, where the Trans-Bi-directional Long Short-Term Memory with Gated Recurrent Unit (Trans-Bi-LSTM-GRU) is adopted for predicting heart diseases. Here, the weights are updated with the developed HCEFSO while validating the training phase. The trained Trans-Bi-LSTM-GRU network is used in the testing phase for predicting heart diseases.

Keywords: Hybrid Chameleon Electric Fish Swarm Optimization, IoT-based heart disease prediction, Gated Recurrent Unit, Weight Updated Trans-Bidirectional Long Short Term Memory

DOI: 10.3233/WEB-230063

Journal: Web Intelligence, vol. Pre-press, no. Pre-press, pp. 1-28, 2024