Using the Extreme Learning Machine (ELM) technique for heart disease diagnosis

One of the most important applications of machine learning systems is the diagnosis of heart disease which affect the lives of millions of people. Patients suffering from heart disease have lot of independent factors such as age, sex, serum cholesterol, blood sugar, etc. in common which can be used very effectively for diagnosis. In this paper an Extreme Learning Machine (ELM) algorithm is used to model these factors. The proposed system can replace a costly medical checkups with a warning system for patients of the probable presence of heart disease. The system is implemented on real data collected by the Cleveland Clinic Foundation where around 300 patients information has been collected. Simulation results show this architecture has about 80% accuracy in determining heart disease.