Improved SPRINT Algorithm and its Application in the Physical Data Analysis

Yazhi Ding, Zhigao Zheng, Ma Rong


In order to determine the human physical condition according to the conventional tested data quickly and accurately, in this paper we proposed a trend selection based scalable parallelizable induction of decision trees algorithm (TESTSPRINT), based on the concept of pure interval and trend selection method. Based on the basic test data such as height, weight and grip strength, we can create a human physical condition decision tree quickly; according to the decision tree we can determine human physical health status quickly. Theoretic analysis and experimental demonstrations show that the algorithms this paper proposed outperforms existing algorithms in time and space complexity, and it was proved fruitful applications in the decision human physical health status with high accuracy.


SPRINT algorithm; Gini index; Physical data; Data mining

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