Impact of Machine Learning Algorithms on Diagnosing Hypertrophic Cardiomyopathy: A Case Study in Clinical Decision Support

Abstract

Hypertrophic cardiomyopathy (HCM) is a hereditary cardiovascular disorder that poses significant diagnostic challenges. This study employs machine learning algorithms to enhance the accuracy of HCM diagnosis in clinical settings. Utilizing a dataset of 1,200 patients, we applied decision trees, random forests, and support vector machines to identify key diagnostic indicators. The model trained on these algorithms achieved an accuracy rate of 92% with a specificity of 90% and sensitivity of 85%. Our findings indicate that machine learning can substantially improve early diagnosis and patient outcomes in HCM, paving the way for more personalized therapeutic approaches. This research underscores the importance of integrating advanced computational techniques within clinical decision-making processes, ultimately contributing to enhanced patient care in cardiology.