Breast cancer prediction based on the machine learning algorithm LightGBM

Nowadays, the idea of Artificial Intelligence (AI) medical detection has aroused great interest around the world. AI has the potential to strengthen medicine in both observation and operation. For instance, AI could catch crucial details that are not intuitive to humans. Robots controlled by AI could also do micro-operations that are extremely hard on human hands. In this study, the author utilizes one of the most focused traditional machine-learning methods, that is the Light Gradient Boosting Machine (LightGBM) algorithm for breast cancer prediction. The LightGBM performs both well on accuracy and speed in the studys experiment. The study applies the bootstrap aggregating (Bagging) method to cope with the over-fitting problem. As the significance of the study, the study shows that the LightGBM can be utilized in designing accurate, fast and cheap medical detection devices. Nevertheless, programmers should handle the over-fitting problem cautiously while building models based on LightGBM. This could help doctors in impoverished areas realize accurate medical detection. People could also do accurate self-diagnosing with a cheap, portable device at home.