This paper identifies prognosis factors for survival in patients with acute myeloid leukemia (AML) using machine learning techniques. We have integrated machine learning with feature selection methods and have compared their performances to identify the most suitable factors in assessing the survival of AML patients. Here, six data mining algorithms including Decision Tree, Random Forrest, Logistic Regression, Naive Bayes, W-Bayes Net, and Gradient Boosted Tree (GBT) are employed for the detection model and implemented using the common data mining tool RapidMiner and open-source R package. To improve the predictive ability of our model, a set of features were selected by employing multiple feature selection methods. The accuracy of classification was obtained using 10-fold cross-validation for the various combinations of the feature selection methods and machine learning algorithms. The performance of the models was assessed by various measurement indexes including accuracy, kappa, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC curve (AUC). Our results showed that GBT with an accuracy of 85.17%, AUC of 0.930, and the feature selection via the Relief algorithm has the best performance in predicting the survival rate of AML patients.
In the present work, some characterization results are established based on the number of observations near the order statistics. Under some conditions, it is shown that the parent distribution can be uniquely determined by the moments of the number of observations in a random sample that fall within a left-hand or right-hand neighborhood of a specific order statistic. It is proved that the underlying distribution F belongs to the class of symmetric distributions if and only if the first moment of the number of observations in the right neighborhood of the kth order statistic and in the left neighborhood of the(n − k + 1)th order statistic from a sample of size n are equal. Also, characterizations of the exponential distribution are presented.
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