Description

Leukemia is not only fatal in nature, it is also extremely expensive to treat. However, leukemia detection at early stage can save lives and money of the affected people, especially children among whom leukemia as a cancer type is very common. In this paper, we explainable supervised machine learning model that accurately predicts the like hood of early-stage leukemia based on symptoms only. In addition, feature selection are performed on the dataset to show the strength of individual features and enhance the performance of the classification models, we are implementing two machine learning algorithm like naïve bayes classifier and support vector machine. Cross-validation is a technique for evaluating ML models by training several ML models on subsets of the available input data and evaluating them on the complementary subset of the data. Use cross-validation to detect over fitting. The performance based SVM algorithm achieved the highest accuracy, precision, recall, and F-measure.