## 5 Conclusions  
In this study, a composite sensing material with low hysteresis and high sensitivity was developed, which was fabricated by mechanically blending 0.665 g magnetic nanoparticles Fe\({}_{3}\)O\({}_{4}\), 1.098 g CNTs, and 0.99 g MLGs as fillers, and 45 g RTV silicone rubber as the substrate. The sensing material demonstrates minimal hysteresis (3.254%) and elevated average sensitivity (0.0462 kPa\({}^{-1}\)) across an extensive pressure range (0-30 kPa), in addition to remarkably low response and recovery durations (0.21/0.32 s) and outstanding repeatability. The derived mathematical model of the hysteresis and sensitivity of the composite sensing material identified the filler concentration and MT as the primary parameters influencing these features. The efficacy of various regression models (RSM, RF, LSTM, and HKOA-LSTM) utilising machine learning techniques to forecast these two attributes was assessed, and the influence of diverse parameters on the model accuracy was examined. The optimal predictive model for the hysteresis performance of sensing materials is HKOA-LSTM, with three hidden layers with 80 neurones per layer, yielding R\({}^{2}\) values of 1 for the training set and 0.981 for the test set. The HKOA-LSTM model precisely forecasts the sensitivity performance of the sensing materials, with R\({}^{2}\) values of 1 for the training set and 0.9803 for the test set. Ultimately, the MORIME algorithm was employed to produce the Pareto frontier solution set for the predictive model, and the optimal solutions derived were juxtaposed with the experimental test values, revealing minimal discrepancies, with a hysteresis error of 0.765% and a sensitivity error of 0.434%. This outcome further substantiates the prospective utility of the optimisation methodology presented in this study for sensor applications. The sensing materials prepared in this study can be applied in the fields of electronic skin, medical monitoring, and intelligent robotics due to their excellent hysteresis, sensitivity, response speed, and repeatability.  
This paper's results validate the machine learning model's efficacy in precisely estimating and predicting the electrical characteristics of composite sensing materials. This method can significantly diminish laboratory scientific tasks, thereby conserving time and expenses related to material procurement and repetitive physical testing. It offers innovative concepts for sensor performance optimisation studies and is crucial for facilitating the large-scale industrial production of flexible pressure sensors.  
Conceptualisation, K.C. and G.H.; methodology, K.C.; software, K.C.; validation, K.C., Q.G. and Y.O.; formal analysis, J.L.; investigation, S.L.; resources, G.H.; data curation, K.C.; writing--original draft preparation, K.C.; writing--review and editing, K.C.; visualisation, S.H.; supervision, S.L.; project administration, J.L.; funding acquisition, G.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Chongqing Natural Science Foundation (Grant No. CSTB2024 NSCQ-MSX0076), Chongqing Municipal Technology Innovation and Application Major Development Special Project (Grant No. CSTB2023 TIAD-STX0037), and Luminal Surgery Robot and Core Components Research Project (Grant No. TC210H031).  
Not applicable.  
The data are contained within the article.  
The authors declare no conflicts of interest.