Sepsis Prediction by Using a Hybrid Metaheuristic Algorithm: A Novel Approach for Optimizing Deep Neural Networks

Abstract

The early diagnosis of sepsis reduces the risk of the patient’s death. Gradient-based algorithms are applied to the neural network models used in the estimation of sepsis in the literature. However, these algorithms become stuck at the local minimum in solution space. In recent years, swarm intelligence and an evolutionary approach have shown proper results. In this study, a novel hybrid metaheuristic algorithm was proposed for optimization with regard to the weights of the deep neural network and applied for the early diagnosis of sepsis. The proposed algorithm aims to reach the global minimum with a local search strategy capable of exploring and exploiting particles in Particle Swarm Optimization (PSO) and using the mental search operator of the Human Mental Search algorithm (HMS). The benchmark functions utilized to compare the performance of HMS, PSO, and HMS-PSO revealed that the proposed approach is more reliable, durable, and adjustable than other applied algorithms. HMS-PSO is integrated with a deep neural network (HMS-PSO-DNN). The study focused on predicting sepsis with HMS-PSO-DNN, utilizing a dataset of 640 patients aged 18 to 60. The HMS-PSO-DNN model gave a better mean squared error (MSE) result than other algorithms in terms of accuracy, robustness, and performance. We obtained the MSE value of 0.22 with 30 independent runs.

Predicting PM10 Concentrations Using Evolutionary Deep Neural Network and Satellite-Derived Aerosol Optical Depth

“…The rapid advancements in computational power and data availability have propelled the application of DNNs across various domains [17][18][19]. Leveraging these advancements, researchers and practitioners are motivated to harness the capabilities of DNNs to revolutionize the prediction of PM10 concentrations, pushing the boundaries of our understanding and predictive accuracy [20,21]. However, the optimization of DNNs to enhance their performance and accuracy remains an uncharted territory.…”

“…Kaya et al [20] introduced a novel hybrid meta-heuristic algorithm for optimizing deep neural network weights, applied to early sepsis diagnosis. This algorithm aims to achieve global optimization using both particle swarm optimization (PSO) and the human mental search (HMS) algorithm for local search.…”

“…where X t+1 d is the updated binary position; Sigmoid (x) is the novel transfer functions; θ is a threshold number ∈ (17, 18,19,20,21); σ is a random number ∈ [0.39, 0.58] ; ϕ is a random number ∈ [0.91, 1]; R is a random number ∈ [0, 1]. Algorithm 1 shows the Pseudo code of the proposed BGWO.…”