Analysis of Influencing Factors in Developing Exercise Prescription for Emergency Patients with Cardiovascular Diseases in the Elderly Based on Machine Learning

The formulation of exercise prescription for elderly patients with emergency cardiovascular disease (CVD) is an important measure to prevent and slow down the incidence of CVD patients. Understanding the factors that influence exercise prescribing in elderly patients with emergency cardiovascular disease is essential for effective prevention and treatment strategies. This study employs machine learning techniques to analyze these factors. The dataset includes demographic information, lifestyle factors, and medical history. Data preprocessing addressed missing values and normalized features. The logistic regression algorithm classified individuals into risk groups based on similarity to others in the dataset. Additionally, the random forest and decision tree algorithms identified key features for predicting exercise prescription for emergency patients with CVD risk. The findings offer insights into exercise prescription for emergency patients with CVD factors in the elderly, aiding targeted interventions for better treatment outcomes.