Construction and evaluation of risk prediction model for Alzheimer's disease: Application of regression analysis methods

Abstract

With the aging of the population, the number of Alzheimer's patients is increasing exponential growth. According to statistics, there are approximately 200 million elderly people aged 60 and above in China, of which 5 to 7 million are affected by Alzheimer's disease. This makes the disease the “king of diseases” that current society needs to focus on. Therefore, early prediction of Alzheimer's disease is particularly important. The existing models that have undergone neuropsychological testing, imaging, and biomarkers are progressing slowly and have significant limitations. Regression methods have significant advantages in predicting Alzheimer's disease by considering multiple variables, establishing quantitative relationship models, and conducting variable selection and validation in the construction of complex disease prediction models. This article evaluates the early prediction of Alzheimer's disease by constructing a regression algorithm model [1].