Exploring the current status and application of machine learning in disease risk prediction models in medical examination

As the aging population problem continues to grow in Asia, an efficient medical examination system based on machine learning will be required. By detecting diseases early, healthcare professionals can provide early interventions and treatments to improve patient's quality of life and overall health outcomes. Therefore, this paper explores the feasibility of utilizing disease risk prediction models based on lifestyle factors in medical examinations and examines possible implications that could arise from applying such models. This study aims to discuss the potential application of machine learning in disease risk prediction models based on lifestyle factors, such as heart disease and cancer, in medical examination. The aim is to use machine learning models in medical examinations to detect diseases in their early stages. The lifestyle factors can be collected through forms and put into a general machine learning model. There will be three machine learning models that could be used as references, which could be used to support the idea's feasibility.