Jingyuan Wu, Qiong Ma, Xun Zhou, Yu Wei, Zhibo Liu, Hongxiang Kang
Segmentation and quantitative analysis of optical coherence tomography (OCT) images of laser burned skin based on deep learning
Biomedical physics & engineering express
study aimed compare accuracy four machinelearning ml algorithms using two classification schemes predict undernutrition based individual household risk factors data publicschool children collected rural province 310 children highly urbanized city 308 children philippines using 24h dietary recalls household socioeconomic demographic survey childrens nutritional risk classified based acceptable macronutrient distribution ranges amdrs developed national academy medicine nam philippine dietary reference intakes pdris four algorithms random forest supportvector machine linear discriminant analysis logistic regression predicted undernutrition sample accuracy sensitivity specificity compared predictions also compared national school feeding programs anthropometric classifications prevalence undernutrition greater nam amdrs 8267 compared pdri amdrs 7871 random forest accurate ml algorithm 7855 able predict undernutrition based household expenditures child household age food insecurity dietary diversity compared anthropometric classification 213 children amdrs classified children risk inadequate dietary intake 477 children random forest algorithm performed best predicting undernutrition among filipino elementary schoolchildren although results could improved bootstrap aggregation amdr classification shows potential targeting feeding beneficiaries however local dietary culture considered development nutrition interventions government use bigdata techniques ml must also address underrepresentation health data collected accessible poor populations risk marginalizing