Disease Risk Prediction by Combining Physical Examination Big Data and Machine Learning Methods

With the advancement of medical technology, the methods of conducting health check-ups have become increasingly diverse. However, conventional health examination items often lack comprehensiveness, leading to the neglect of important tests such as bone density, thyroid nodules, and cancer cell risk. This paper explores the application of machine learning techniques to analyze health check-up big data for predicting additional potential diseases. We will focus on analyzing basic health examination parameters, considering factors such as patient age and gender. Various algorithms, including neural networks, principal component analysis, and logistic regression, were employed to predict thyroid nodules. The findings indicate the effectiveness of the model, and insights on feature importance are provided. We hope this study contributes to enhancing the value of health check-up data and promotes research in early disease prediction.