In recent medical field advancements, many medicines and high-end curable treatments has been discovered for tumor patients. The key purpose of this work is to center on prediction of tumor in liver of human body by data mining techniques and machine learning algorithms. The therapeutic administrations sector accumulates a massive amount of data that has not been appropriately mined and utilized for effective use. The disclosure of these cloaked real-time gatherings and links is routinely overlooked. Our work focuses on this aspect of medical discovery by translating knowledge about liver tumors to produce intelligent clinical decisions and emotionally supportive networks to assist clinicians. Here nine attributes of blood test values will be used as data set. The detection of tumor in early stage is most important factor to consider for patient's cure. Hence classification of tumors by classification models with the data mining predictive algorithms is performed. Our evaluation obtains better accuracy comparing with different classifier algorithms.
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