Bulletin of Electrical Engineering and Informatics Vol. 13, No. 2, April 2024, pp. 1276~1285 ISSN: 2302-9285, DOI: 10.11591/eei.v13i2.6280  1276 Journal homepage: http://beei.org Predicting lung cancer risk using explainable artificial intelligence Shahin Shoukat Makubhai, Ganesh R. Pathak, Pankaj R. Chandre Department of Computer Science and Engineering, MIT School of Computing, MIT Art, Design and Technology University, Loni Kalbhor, India Article Info ABSTRACT Article history: Received Mar 20, 2023 Revised Jul 17, 2023 Accepted Oct 6, 2023 Lung cancer is a lethal disease that claims numerous lives annually, and early detection is essential for improving survival rates. Machine learning has shown promise in predicting lung cancer risk, but the lack of transparency and interpretability in black-box models impedes the understanding of factors that contribute to risk. Explainable artificial intelligence (XAI) can overcome this limitation by providing a clear and understandable approach to machine learning. In this study, we will use a large patient record dataset to train an XAI-based model that considers various patient information, including lifestyle factors, clinical data, and medical history, for predicting lung cancer risk. We will use different XAI techniques, including decision trees, partial dependence plots, and feature importance, to interpret the model’s predictions. These methods will provide healthcare professionals with a transparent and interpretable framework for screening and treatment decisions concerning lung cancer risk. Keywords: Explainable artificial intelligence Interpretability Lung cancer Machine learning Prediction Risk factors This is an open access article under the CC BY-SA license. Corresponding Author: Shahin Shoukat Makubhai Department of Computer Science and Engineering, MIT School of Computing MIT Art, Design and Technology University Loni Kalbhor, India Email: shahin.makubhai@mituniversity.edu.in 1. INTRODUCTION Lung cancer is one of the most common and deadly forms of cancer worldwide. It is estimated that lung cancer accounts for 2.09 million new cases and 1.76 million deaths each year. Early detection and accurate diagnosis of lung cancer are essential for improving patient outcomes and reducing mortality rates [1]. A branch of artificial intelligence (AI) known as explainable AI (XAI) aims to develop systems that are simple enough for people to understand [2]. XAI is especially significant in the healthcare industry, where gaining the confidence of physicians and patients requires being able to explain how a machine learning model is making predictions. Creating a machine learning model that can estimate a person’s probability of getting lung cancer based on various risk factors is required to predict lung cancer risk using XAI. Personal traits like age, sex, smoking history, family history, and risk factors include examples such as being exposed to environmental toxins [3]. It would be necessary to train a sizable dataset of lung cancer patients and healthy individuals for the XAI model used to predict the chance of developing lung cancer [4]. To guarantee that the dataset is a representative of the general population and contains a diverse range of people with varying risk factors, it would need to be carefully curated. Once trained, the XAI model can be used to estimate a person’s chance of developing lung cancer based on their risk factors. Additionally, the XAI model would be able to explain how it got to its prediction [5].