Title: SExpSTO-ResNet: serial exponential Siberian tiger optimisation algorithm-based ResNet50 for heart disease prediction

Authors: M.G. Chitra; Ramya Govindaraj

Addresses: School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore. Tamilnadu, India ' School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore. Tamilnadu, India

Abstract: This work designs a technique for heart disease prediction by deep learning model. The pre-processing stage is primary phase, and it is considered as most significant process to enhance the technique's performance. The main aim of pre-processing is to transfer raw data into processable data. Moreover, missing data imputation is exploited to carry out pre-processing that is employed to eradicate infinity values for effectual processing. Subsequently, by using the temporal convolutional network, feature fusion is done. Finally, heart disease prediction is done by using ResNet 50 that is trained by proposed serial exponential Siberian tiger optimisation named (SExpSTO) scheme that is derived by combining serial exponential weighted moving average in Siberian tiger optimisation (STO). The performance analysis of proposed algorithm is executed by considering parameters, like accuracy, sensitivity, and specificity. Finally, the experimentation evaluation is performed with maximum accuracy of 95%, maximum sensitivity of 97%, and maximum specificity of 94%.

Keywords: healthcare; heart disease; feature fusion; deep learning; optimisation algorithm; Siberia.

DOI: 10.1504/IJBIC.2026.153432

International Journal of Bio-Inspired Computation, 2026 Vol.27 No.3, pp.157 - 168

Received: 11 Apr 2024
Accepted: 30 Oct 2024
Published online: 07 May 2026 *

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