Machine Learning-Driven Prediction of Cerebral Small Vessel Disease Based on Electromagnetic Device Data

With the continuous development of modern medicine, electromagnetic devices have been widely used in the clinical diagnosis of various diseases, which has had a profound impact on human health and quality of life.However, current predictive methods for cerebral small vessel disease based on electromagnetic device measurement data are still limited and mostly constrained by traditional statistical methods, making it difficult to fully exploit complex data patterns.To address this issue, this study proposed a prediction method for cerebral small vessel disease based on ensemble learning.Experimental result showed that the proposed model predicts an AUC value of up to 0.919, demonstrating the enormous potential of machine learning and ensemble learning methods in predicting cerebral small vessel disease.This research provides a new technological path for the application of electromagnetic device data in the early detection and diagnosis of cerebral small vessel disease.