Optimized Deep Representation Learning Based Healthcare Diagnosis and Classification Model

In recent times, machine learning (ML) and deep learning (DL) based classification models have been significantly employed in the healthcare sector to determine diseases. Since manual disease diagnosis process is difficult and laborious, automated tools for disease diagnosis have been developed. The ML and DL models can be employed for various disease diagnosis using healthcare data and medical images. Therefore, this article presents an optimal deep representation extreme learning machine (ODR-ELM) technique for medical data classification. The presented ODR-ELM technique mainly intends to identify the occurrence of the disease using the patient medical data. The ODR-ELM technique primarily applies data preprocessing to enhance data quality. Secondly, the DR-ELM classification model is utilized to classify the existence or nonexistence of the disease. To boost the classifier efficacy of the DR-ELM technique, the glowworm swarm optimization (GSO) technique is utilized. The result analysis of the ODR-ELM model take place using three benchmark medical dataset and the results are inspected under several aspects. The simulation results reported the better performance of the ODR-ELM technique interms of different measures.