International Journal of Reconfigurable and Embedded Systems (IJRES) Vol. 13, No. 3, November 2024, pp. 724~734 ISSN: 2089-4864, DOI: 10.11591/ijres.v13.i3.pp724-734  724 Journal homepage: http://ijres.iaescore.com Precision medicine in hepatology: harnessing IoT and machine learning for personalized liver disease stage prediction Satyaprakash Swain 1,2 , Mihir Narayan Mohanty 3 , Binod Kumar Pattanayak 1 1 Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India 2 Department of Computer Science and Engineering, Institute of Management and Information Technology, Biju Patnaik University of Technology, Odisha, India 3 Department of Electronics and Communication Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India Article Info ABSTRACT Article history: Received Oct 25, 2023 Revised Mar 24, 2024 Accepted Apr 19, 2024 In this research, we used a dataset from Siksha ‘O’ Anusandhan (S’O’A) University Medical Laboratory containing 6,780 samples collected manually and through internet of things (IoT) sensor sources from 6,780 patients to perform a thorough investigation into liver disease stage prediction. The dataset was carefully cleaned before being sent to the machine learning pipeline. We utilised a range of machine learning models, such as Naïve Bayes (NB), sequential minimal optimisation (SMO), K-STAR, random forest (RF), and multi-class classification (MCC), using Python to predict the stages of liver disease. The results of our simulations demonstrated how well the SMO model performed in comparison to other models. We then expanded our analysis using different machine learning boosting models with SMO as the base model: adaptive boosting (AdaBoost), gradient boost, extreme gradient boosting (XGBoost), CatBoost, and light gradient boosting model (LightGBM). Surprisingly, gradient boost proved to be the most successful, producing an astounding 96% accuracy. A closer look at the data showed that when AdaBoost was combined with the SMO base model, the accuracy results were 94.10%, XGBoost 90%, CatBoost 92%, and LightGBM 94%. These results highlight the effectiveness of proposed model i.e. gradient boosting in improving the prediction of liver disease stage and provide insightful information for improving clinical decision support systems in the field of medical diagnostics. Keywords: Internet of things sensors K star Multi-class classification Naïve Bayes Random forest Sequential minimal optimisation This is an open access article under the CC BY-SA license. Corresponding Author: Satyaprakash Swain Department of Computer Science and Engineering, Institute of Technical education and Research Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar, Odisha, India Email: satyaimit@gmail.com 1. INTRODUCTION Millions of people worldwide are affected by liver disease, which also places a heavy cost on healthcare systems [1] around the world. Accurate staging is required for effective care of liver diseases, in addition to prompt diagnosis, in order to direct the right clinical interventions. While reliable, conventional techniques of liver disease diagnosis have some drawbacks, particularly when it comes to assessing the severity and course of the disease. In this context, combining machine learning and internet of things (IoT) technology presents a viable path for enhancing patient care and diagnostic accuracy.