force　prediction　is　widely　used　in　the　rehabilitation　of　the　arm　and　prosthetic　control.　To　investigate　the　effects　of　different　measurement　positions　and　feature　parameters　on　the　results　of　handgrip　force　prediction,　a　model　based　on　electrical　impedance　myography　(EIM)　and　long　short-term　memory　(LSTM)　networks　was　proposed　to　compare　and　determine　a　better　scheme　for　handgrip　force　prediction.　We　conducted　the　signal　acquisition　experiments　of　impedance　and　handgrip　force　on　the　anterior　forearm　muscles　and　brachioradialis　muscle.　Afterwards,　three　evaluation　metrics　were　introduced　to　compare　the　prediction　results　of　various　models,　and　the　variability　between　models　was　analyzed　using　paired　sample　t-tests.　The　results　showed　that　the　model　of　handgrip　force　prediction　based　on　anterior　forearm　muscles　exhibited　better　performance　in　predicting.　The　evaluation　metrics　of　R2,　explained　variance　score　(EVS)　and　normalized　mean　square　error　(NMSE)　for　the　model　fusing　the　feature　parameters　resistance　(R)　and　reactance　(X)　were　0.9023,　0.9173　and　0.0114,　respectively.　Therefore,　the　feature　parameters　fusing　R　and　X　are　the　optimal　input　for　the　handgrip　force　prediction　model.　The　anterior　forearm　muscles　are　the　preferred　position　for　impedance　measurement　over　the　brachioradialis　muscle.　This　paper　validated　the　feasibility　of　EIM　for　handgrip　force　prediction　and　provided　a　new　reference　and　implementation　scheme　for　muscle　rehabilitation　training　and　prosthetic　control.