چکیده

According to the WHO (World Health Organization) Cardiovascular (Heart Failure) is a fatal disease that cause estimated 17.9 million people death every year. Heart Disease risk increase due to cholesterol, overweight and hypertension in short due to harmful behavior, heart disease risk increase [1]. Further according to the American Heart Association [2] symptoms(complement) are leg swelling, sleep problem, high heart rate and cough. Diagnosis is difficult due to the common symptoms because these symptoms associate with other diseases. The collection of medical data help physician to diagnosis the disease. Machine learning playing an important role in medical field as diagnosis of disease. Machine learning is used where data is large and difficult to extract useful information from data. In this study, we have used machine learning approach with rapid miner tool for heart disease classification. The experiment results show that performance of Logistic Regression is effective with accuracy of 85.22% than other considered machine learning models.

کلیدواژه:

Heart Failure

Machine Learning

Principal Component Analysis

Classification

نویسندگان :

Muhammad Asad Arshed^*1, Fasial Riaz^2
 1,2School of Systems and Technology (SST), University of Management and Technology, Lahore, Pakistan.

DOR:

دانلود فایل مقاله                    منابع                   XML