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During the last decade, blood cancer has risen to a point of worry, and early diagnosis helps start the right therapy. Diagnosis of blood cancer using leukemia microarray gene information and machine learning techniques has become an important medical study today. This research recommends a supervised machine learning approach to blood cancer disease prediction. The leukemia microarray gene registry database containing 22,283 genes is used in this new study. To solve imbalanced and high-dimensional dataset issues, the ADASYN resampling and Chi-squared feature selection methods are used. ADASYN designs artificial data to make the database balanced for each target class, and Chi2 selects the best features out of 22,283 to train learning models. A hybrid logistics vector tree classifier is used for classification. A logistic regression, support vector classifier, and an additional tree classifier are all suggested for classification. With ADASYN and Chi2 techniques, LVTrees outperform all other models with a high percentage of accuracy.
Source link: https://doi.org/10.1038/s41598-022-04835-6
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