Title: An optimised deep AutoEncoder with enhanced extreme learning machine model for heart disease prediction and classification

Authors: M. Duraisamy; S.P. Balamurugan

Addresses: Department of Computer Science, Government Arts and Science College, Tirupattur, 635901, Tamilnadu, India ' Department of Computer and Information Science, Annamalai University, Annamalainagar, 608002, Tamilnadu, India

Abstract: This research proposes a comprehensive and innovative approach that includes an optimised deep auto encoder with an enhanced extreme learning machine (ODAE-EELM) model. The model combines a novel deep encoder with red deer optimisation (RDO) for feature selection, and an extreme learning machine (ELM) with Stochastic gradient descent (SGD) optimisation for classification. The presented ODAE-EELM model employs pre-processing to convert the actual data into a usable format. Next, the integration of RDO in the encoder optimises the feature selection process by mimicking the foraging behaviour of red deer to enhance exploration and exploitation, thereby yielding more robust and discriminative features. The extreme learning machine is employed for the classification stage due to its simplicity, efficiency, and ability to handle high-dimensional data effectively. The ELM model is optimised using Stochastic Gradient Descent, which ensures faster convergence and efficient utilisation of computational resources. Experimental results shows, our proposed method attained the maximum accuracy of 99%.

Keywords: red deer optimisation; deep auto encoder; feature selection; extreme learning machine; Stochastic gradient descent; heart disease classification.

DOI: 10.1504/IJSSE.2026.153650

International Journal of System of Systems Engineering, 2026 Vol.16 No.2, pp.177 - 193

Received: 23 Aug 2023
Accepted: 27 Dec 2023
Published online: 21 May 2026 *

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