Diagnosis of Acute Myeloid Leukaemia Using Machine Learning
This work addresses the problem of accurate AML diagnosis for medical practitioners and patients, but it appears incremental as it applies standard machine learning methods to a new dataset with specific features.
The researchers tackled the problem of diagnosing acute myeloid leukemia (AML) by training a machine learning model on a multicentric dataset of 2177 individuals using 26 probe sets and age as features, achieving 99.94% accuracy and an F1-score of 0.9996, which they claim is the best performance in the literature for AML prediction.
We train a machine learning model on a dataset of 2177 individuals using as features 26 probe sets and their age in order to classify if someone has acute myeloid leukaemia or is healthy. The dataset is multicentric and consists of data from 27 organisations, 25 cities, 15 countries and 4 continents. The accuracy or our model is 99.94\% and its F1-score 0.9996. To the best of our knowledge the performance of our model is the best one in the literature, as regards the prediction of AML using similar or not data. Moreover, there has not been any bibliographic reference associated with acute myeloid leukaemia for the 26 probe sets we used as features in our model.