Quantitative models are essential in precision medicine that can be used to predict health status and prevent disease and disability. Current radiomics models for clinical outcome prediction often depend on huge amount of image features and may include redundant information and ignore individual feature importance. In this work, we propose a prognostic discrimination ranking strategy to select the most relevant image features for image assisted clinical outcome prediction. Firstly, a redundancy and prognostic discrimination evaluation method is proposed to evaluate and rank a large number of features extracted from images. Secondly, forward sequential feature selection is performed to select the top ranked relevant features in each discriminate quantization. Finally, representative vectors are generated by the fusion of pivotal clinical parameters and selected image signatures to be fed into a classification model. The proposed model was trained and tested over 70 patient studies with six MR sequences and four clinical parameters from ISLES challenges. The evaluations using ROC curves demonstrated the improved performance over five other feature selection models where the proposed model achieved AUCs of 0.821, 0.968, 0.983, 0.896 and 1 when predicting five clinical outcome scores respectively.