Close monitoring ICU patients is a necessity for health care providers. Prediction of mortality of ICU patients based on the monitored data is an active research area. If the probability of survival (or death) of a patient could be predicted early enough, proper and timely attention could be given to the patient, saving the patients life. Most of the existing work in this regard try to predict mortality or deterioration of ICU patients using vital signs. However, our work is focused to predict the mortality of the patients using only the clinical test results. The reason for using only clinical tests instead of using vital signs is not to propose a new method, but to complement the use of vital signs for the prediction. The main goal of this study is to identify the challenges associated with utilizing clinical test results for the prediction, and propose efficient and scalable solutions. To achieve this goal, we propose a novel technique that we call 'feature vector compaction'. This is different from feature selection or reduction in that we do not discard any feature, rather we try to minimize the vacuum (i.e., missing data) in the feature vector. We have evaluated our proposed technique on a real ICU patients data and achieved notable success in reducing 50% or more of the feature vector, while improving the prediction accuracy by 2-5% and achiving more than 200% speedup in most cases. We believe that the proposed technique will be very useful for Big Data analytic techniques in the field of clinical and health informatics wherever the data follows similar characteristics.