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Research Article Open Access
Missing data can be recreant because it is complicated to identify the problem. Missing data can cause critical problems. First, most statistical procedures automatically eliminate cases with missing data. . Second, the analysis might run but the results may not be statistically significant because of the small amount of input data. In this paper we inspect the enforcement of two unusual data imputation process in a task where the aim is to conclude the probability of finding missing data in blood cancer and occurrence of blood cancer using improved ID3 algorithm. Cancer is one of the deadliest diseases found among many people across the world. Our project aims at helping the medical practitioners to diagnose the patients at the early stage which can reduce the number of deaths.
Data mining, missing values, ID3 Algorithm, data migration, decision tree classification, multi array model, data density clustering., Blood Clot Symptoms,Blood Cancer Clinical Trials,Cancer Signaling Array