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

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%.