Extreme learning machine (ELM) is a rapid classifier, evolved for batch learning mode which is not suitable for sequential input. As retrieving of data from new inventory which is leads to time extended process. Therefore, online sequential ELM (OSELM) algorithm is progressed to handle the sequential input in which data is read 1 by 1 or chunk by chunk mode. The overall system generalization performance may devalue because of the amalgamation of random initialization of OS-ELM and the presence of redundant and irrelevant features. To resolve the said problem, this paper proposes a correspondence improved genetic optimized feature selection paradigm for sequential input (IG-OSELM) for radial basis or function by using clinical datasets. For performance comparison, the proposed paradigm experimented and evaluated for ELM, improved genetic optimized for ELM classifier (IG-ELM), OS-ELM, IG-OSELM. Experimental results are calculated and analyzed accordingly. The comparative results analysis illustrates that IG-ELM provides 10.94% improved accuracy with 43.25% features as compared to ELM.