[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121220-en":3,"doc-seo-121220-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121220,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Precision medicine in hepatology - harnessing IoT and machine learning for personalized liver disease stage prediction","Research investigates precision medicine in hepatology by building a liver disease stage prediction pipeline that combines IoT-derived patient data with machine learning. A cleaned dataset of 6,780 samples from the university medical laboratory is used to train multi-class models, including Naïve Bayes, SMO, K-STAR, and random forest. Boosting approaches are evaluated with SMO as the base model, where gradient boosting achieves the highest accuracy, reaching 96%. Findings support clinical decision support for more accurate, stage-aware diagnostics.","Precision medicine in hepatology: harnessing IoT and machine learning for personalized liver disease stage prediction  \nSatyaprakash Swain1,2, Mihir Narayan Mohanty3, Binod Kumar Pattanayak1  \n1Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India  \n2Department of Computer Science and Engineering, Institute of Management and Information Technology, Biju Patnaik University of Technology, Odisha, India  \n3Department of Electronics and Communication Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India  \nArticle history:  \nReceived Oct 25, 2023 Revised Mar 24, 2024 Accepted Apr 19, 2024  \nKeywords:  \nInternet of things sensors K star  \nMulti-class classification Naïve Bayes  \nRandom forest Sequential minimal optimisation  \nCorresponding Author:  \nIn 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.  \nThis is an open access article under the CC BY-SA license.  \nSatyaprakash Swain  \nDepartment of Computer Science and Engineering, Institute of Technical education and Research Siksha ‘O’ Anusandhan (Deemed to be University)  \nBhubaneswar, Odisha, India  \nEmail: [satyaimit@gmail.com](satyaimit@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nMillions 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.  \nThe goal of this study is to use IoT secure framework [2] and machine learning to predict the stages of liver disease [3], taking into account the important differences between stages 1 to 4. Also, this study investigates the viability of using a collection of machine learning models to accomplish this crucial diagnostic goal by leveraging a real time dataset of 6 ,780 samples which are obtained through a combination of manual sample tests and data collected through IoT sensors from the patients of Siksha ‘O’ Anusandhan (S’O’A) University Medical Laboratory, Bhubaneswar. The potential to improve live","cbCaij3tODGaXLdF","https://ap.wps.com/l/cbCaij3tODGaXLdF","pdf",825564,1,11,"English","en",105,"# Abstract\n# Introduction\n## Motivation and clinical need\n## Study goal and dataset overview\n## IoT sensor parameters and feature scope\n# Machine Learning Models\n## Baseline classification models\n## Boosting strategies and performance comparison","[{\"question\":\"What is the main objective of the study on liver disease staging?\",\"answer\":\"To predict liver disease stages (stages 1 to 4) using an IoT-secured data framework and machine learning, improving diagnostic accuracy for clinical decision support.\"},{\"question\":\"How is the dataset for modeling collected and prepared?\",\"answer\":\"The study uses 6,780 samples from the Siksha ‘O’ Anusandhan University Medical Laboratory, collected manually and via IoT sensors, and then performs careful dataset cleaning before training models.\"},{\"question\":\"Which machine learning approaches are compared, and what is the best reported performance?\",\"answer\":\"Baseline models include Naïve Bayes, SMO, K-STAR, and random forest. Boosting models with SMO as the base are also tested; gradient boosting is reported as best, reaching about 96% accuracy.\"}]","Precision medicine in hepatology - harnessing IoT and machine learning for personalized liver disease stage prediction | PDF",1785734419,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"precision-medicine-in-hepatology-harnessing-iot-and-machine-learning-for-personalized-liver-disease-stage-prediction","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/precision-medicine-in-hepatology-harnessing-iot-and-machine-learning-for-personalized-liver-disease-stage-prediction/121220/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study on liver disease staging?","Question",{"text":75,"@type":76},"To predict liver disease stages (stages 1 to 4) using an IoT-secured data framework and machine learning, improving diagnostic accuracy for clinical decision support.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the dataset for modeling collected and prepared?",{"text":80,"@type":76},"The study uses 6,780 samples from the Siksha ‘O’ Anusandhan University Medical Laboratory, collected manually and via IoT sensors, and then performs careful dataset cleaning before training models.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approaches are compared, and what is the best reported performance?",{"text":84,"@type":76},"Baseline models include Naïve Bayes, SMO, K-STAR, and random forest. 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