Diagnosis of Acute Myeloid Leukaemia Using Machine Learning

TL;DR

This paper presents a machine learning model trained on multicentric data to accurately diagnose acute myeloid leukaemia with unprecedented performance, using 26 probe sets and age as features.

Contribution

It introduces the highest-performing AML classification model to date, utilizing novel feature sets without prior bibliographic references.

Findings

Model accuracy of 99.94%

F1-score of 0.9996

First to use these probe sets for AML prediction

Abstract

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.