Explainable Boosting Machines for Lung Cancer Prediction and Explanation

Clinical predictive models have played an important role in healthcare. An important task in lung cancer healthcare is to identify those participants involved in a screening program with higher lung cancer risk from a selected population. More interestingly, Electronic Healthcare Records (EHRs) data can be acquired from primary care and have been used to emulate a screening program. An example of such EHR dataset is Clinical Practice Research Datalink (CPRD) that covers 4.5% UK population. In this paper, we provide a worked example for such task while employing Explainable Boosting Machine (EBM) as the predictive model and using CPRD dataset as the EHRs. EBM is a prominent example of inherently interpretable models (i.e., IIM). IIMs can predict target variables and model explanation simultaneously. More importantly, EBMs represent a family of non-linear IIMs. This kind of generalisation presents a significant extension of logistic regression. EBMs have been developed as an end-to-end system at Microsoft Research. It provide powerful visualisation tools for evaluating both model prediction and explanation. On the other hand, EBM users like to know more technical details about EBM itself. Thus, we provide a brief introduction to Generalised Additive Model, Gradient Boosting, Boosted Trees, and Bagging Ensemble. Finally, we further provide two EBM-based Use Cases in healthcare domain as well as an illustrative example of lung cancer prediction and explanation.