An Explainable Artificial Intelligence Based Early Lung Cancer Risk Prediction Using LightGBM
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Keywords:
Cancer Risk Prediction, Machine Learning, Data Engineering, Explainable Artificial IntelligenceAbstract
Lung cancer is the most common cancer in the world and is also the deadliest. Early diagnosis
can improve patients’ life expectancy and reduce the cost of treatment. The aim of this study is to help
physicians and patients by using explainable artificial intelligence methods to early diagnose risk of lung
cancer. This study will create an opportunity for physicians to make early diagnosis and treatment
strategies for patients. In this study, a machine learning model was developed to predict the risk of lung
cancer by using the LightGBM algorithm. Furthermore, the SHAP method is used to explain why and
how the model's predictions are made, thus making the AI model reliable. These explanations increase the
acceptability and reliability of the predictions made by model, while helping physicians and patients
understand the model's decisions. The results obtained show that the developed LightGBM model can
predict the risk of lung cancer with a 100% accuracy rate. The model has achieved great results in terms
of accuracy rate and sensitivity. In addition, the SHAP analyses explain which features each of the
model's predictions is based on, which will increase the confidence of physicians and patients in the
decisions of artificial intelligence.
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