Double exponential smoothing slime mould algorithm for disease detection in IoT healthcare system

Abstract

This paper presents an algorithm, called the double exponential smoothing slime mould algorithm (DeSSMA), which is formulated to train deep learning models for the precise detection of diseases in patients. The DeSSMA is designed by integrating the principles of double exponential smoothing with the slime mould algorithm. The parameters, including energy depletion, link lifetime (LLT), and distance, are considered by the proposed DeSSMA as objectives aimed at optimizing data routing efficiency. In the base station, a deep residual network (DRN) is trained using the proposed DeSSMA algorithm, which is utilized for disease detection following the processes of data preprocessing, augmentation, and feature selection. Finally, performance evaluation of the DeSSMA-DRN framework is conducted using metrics such as energy consumption, LLT, accuracy, sensitivity, specificity, and receiver operating characteristic. The findings reveal that the proposed framework achieved a minimal energy depletion rate of 0.412 (J), an LLT rate of 0.318, an increased accuracy rate of 0.959, a high sensitivity rate of 0.967, and a specificity rate of 0.931.