Obesity is a major risk factor for chronic diseases,underscoring the need for early diagnosis and effective management.This study presents a novel expert system designed to accurately classify obesity levels and provi...Obesity is a major risk factor for chronic diseases,underscoring the need for early diagnosis and effective management.This study presents a novel expert system designed to accurately classify obesity levels and provide personalised treatment recommendations.Five machine learning algorithms—decision tree,random forest,multinomial logistic regression(MLR),Naive Bayes,and support vector machine(SVM)—were evaluated using the SEMMA data mining methodology and the tidymodels framework.MLR demonstrated the highest accuracy(97.48%)and was selected as the final model.The system features a userfriendly interface built with R Shiny,facilitating real-time interaction and a seamless user experience.Treatment recommendations are generated through if-then rule-based logic,ensuring tailored guidance for each obesity category.Comparative analysis highlights the system's superior diagnostic accuracy and practical application in treatment guidance.Its accessibility,particularly in underserved rural populations,enhances public health outcomes by enabling early diagnosis,targeted interventions,and proactive obesity management.展开更多
基金supported by the National Research,Development and Innovation Office(NKFIH)(Grant 2024-1.2.3-HU-RIZONT-2024-00030).
文摘Obesity is a major risk factor for chronic diseases,underscoring the need for early diagnosis and effective management.This study presents a novel expert system designed to accurately classify obesity levels and provide personalised treatment recommendations.Five machine learning algorithms—decision tree,random forest,multinomial logistic regression(MLR),Naive Bayes,and support vector machine(SVM)—were evaluated using the SEMMA data mining methodology and the tidymodels framework.MLR demonstrated the highest accuracy(97.48%)and was selected as the final model.The system features a userfriendly interface built with R Shiny,facilitating real-time interaction and a seamless user experience.Treatment recommendations are generated through if-then rule-based logic,ensuring tailored guidance for each obesity category.Comparative analysis highlights the system's superior diagnostic accuracy and practical application in treatment guidance.Its accessibility,particularly in underserved rural populations,enhances public health outcomes by enabling early diagnosis,targeted interventions,and proactive obesity management.
