A Study of Heart Disease Diagnosis Using Machine Learning and Data Mining

This study explores the application of machine learning and data mining techniques for the diagnosis of heart disease. We focus on the development and evaluation of various machine learning models, including logistic regression, decision trees, random forests, support vector machines, and neural networks. These models are trained and tested on a comprehensive dataset, with performance assessed using accuracy, sensitivity, specificity, and the area under the ROC curve. Additionally, data mining techniques such as association rule mining and cluster analysis are employed to uncover underlying patterns and relationships within the data. The integration of these methods provides a multifaceted approach to diagnosing heart disease, offering insights into the heterogeneity of the condition and revealing subtypes with distinct characteristics. The study concludes that machine learning and data mining techniques have significant potential to enhance diagnostic accuracy and inform personalized treatment strategies in the field of cardiology.