Symptom Based Explainable Artificial Intelligence Model for Leukemia Detection

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, specially children among whom leukemia as a cancer type is very common. In this paper, we propose an explainable supervised machine learning model that accurately predicts the likelihood of early-stage leukemia based on symptoms only. The proposed model is developed based on primary data collected from two major hospitals in Bangladesh. Sixteen features of the datasets are collected through a survey on leukemia and non-leukemia patients in consultation with a specialist physician. Our explainable supervised model is based on a decision tree classifier which provides significantly better results compared to other algorithms and generates explainable rules that are ready to use. We have employed Apriori algorithm for generating explainable rules for leukemia prediction. In addition, feature analysis and feature selection are performed on the dataset to show the strength of individual features and enhance the performance of the classification models. Several classifiers are experimented on the dataset to show how the proposed model that is simple yet explainable, performs significantly better compared to most other models that we have used. The decision tree model proposed in our experiments has achieved 97.45% of accuracy, 0.63 of Mathew’s Correlation Coefficient (MCC) and 0.783 of area under Receiver Operating Characteristic (ROC) curve on the test set. We have also made the dataset and the source code of the methods used in this work available for future use by the researchers.