Background　and　Objective:　The　term　＇obesity＇　refers　to　excessive　body　fat,　and　it　is　a　chronic　disease　associated　with　various　complications.　Although　a　range　of　techniques　for　body　fat　estimation　have　been　developed　to　assess　obesity,　they　are　typically　associated　with　high-cost　tests　requiring　special　equipment.　Accurate　prediction　of　the　body　fat　percentage　based　on　easily　accessed　body　measurements　is　thus　important　for　assessing　obesity　and　its　related　diseases.　This　paper　presents　an　improved　relative　error　support　vector　machine　approach　to　predict　body　fat　in　a　cost-effective　manner.　Methods:　Our　proposed　method　introduces　a　bias　error　control　term　into　its　objective　function　to　obtain　an　unbiased　estimation.　Feature　selection　is　also　utilised,　by　removing　either　redundant　or　irrelevant　features　without　incurring　much　loss　of　information,　to　further　improve　the　prediction　accuracy.　In　addition,　the　Wilcoxon　rank-sum　test　is　used　to　validate　if　the　performance　of　our　proposed　method　is　significantly　better　than　other　prediction　models　being　compared.　Results:　Experimental　results　based　on　four　evaluation　metrics　show　that　the　proposed　method　is　able　to　outperform　other　prediction　models　under　comparison.　Considering　the　characteristics　of　different　features　(e.g.,　body　measurements),　we　show　that　applying　feature　selection　can　further　improve　the　prediction　performance.　Statistical　analysis　carried　out　confirms　that　our　proposed　method　has　obtained　significantly　better　results　than　other　compared　methods.　Conclusions:　We　have　proposed　a　new　approach　to　predict　the　body　fat　percentage　effectively.　This　approach　can　provide　a　good　reference　for　people　to　know　their　body　fat　percentage　with　easily　accessed　measurements.　Statistical　test　results　based　on　the　Wilcoxon　rank-sum　test　not　only　show　that　our　proposed　method　has　significantly　better　performance　than　other　prediction　models　being　compared,　but　also　confirm　the　usefulness　of　incorporating　feature　selection　into　the　proposed　method.　(c)　2020　Elsevier　B.V.　All　rights　reserved.