Research on a Prediction Method for Atrial Fibrillation Based on LDA Machine Learning

This paper presents a method for predicting atrial fibrillation (AF) aimed at enhancing the efficiency of early detection and personalized patient care. The method utilizes an LDA machine learning model to predict the risk of AF in real-time based on clinical data. This model is integrated into a decision support framework, assisting healthcare providers in making data-driven decisions. Results indicate a significant improvement in predictive accuracy, highlighting its potential impact on healthcare management. The proposed prediction method effectively bridges the gap between traditional clinical approaches and modern analytics-driven decision-making. To address the challenges of early prediction and management of AF patients, this paper proposes a machine learning-based AF prediction method. By optimizing model parameters and comparing the predictive performance of various machine learning algorithms, the optimal model is selected for analysis and prediction. The results show that the linear discriminant analysis (LDA) model performs best in predicting atrial fibrillation. This method can help patients detect AF risk at an early stage.