In　practical　applications　such　as　medical　diagnosis　and　group　decision　making,　the　potential　structural　information　contained　in　multi-dimensional　features　in　the　form　of　group　domains　plays　an　important　role.　However,　most　existing　feature　selection　methods　adopt　transformed　feature　spaces　for　group　structure　analysis,　which　lack　intrinsic　semantic　information　interpretation.　Meanwhile,　fuzzy　and　uncertain　heterogeneous　data　acquired　from　multiple　devices　increase　the　difficulty　of　task　learning.　Motivated　by　these　two　issues,　this　work　devises　a　Heterogeneous　Feature　Selection　method　with　Group　Structure　Mining　in　fuzzy　decision　systems　(HFS-GSM),　which　follows　the　principle　of　one　＇strategy＇　and　one　＇mechanism＇.　Specifically,　a　feature　group　generation　strategy　based　on　fuzzy　approximation　Markov　blanket　is　first　designed　for　mining　features　with　group　structure,　which　introduces　the　concept　of　Markov　blanket　into　the　fuzzy　rough　set　and　utilizes　the　idea　of　approximation　and　fuzzy　uncertainty　measures.　Then,　a　fuzzy　dependency-based　overlapping　group　elimination　mechanism　is　proposed　by　attribution　division,　which　avoids　local　redundancy　while　preserving　global　discriminative　information.　Furthermore,　the　effectiveness　of　HFS-GSM　is　verified　in　comparison　with　seven　representative　feature　selection　methods　on　publicly　available　medical　datasets.　Finally,　medical　diagnosis　data　provided　by　a　hospital　are　obtained　to　demonstrate　the　reliability　and　utility　of　HFS-GSM　in　practical　applications.　©　2025　Elsevier　B.V.