In health care industry, data mining plays a very vital role in the prediction of heart diseases. For detecting a disease, number of tests should be required from the patient. But using data mining, the number of tests should be reduced. This reduced test plays an important role in time ...
Coronary heart disease (CHD) is a serious cardiovascular disease (CVD)1that not only has a high mortality rate but also patients are prone to the risk of recurrence even after they are cured and discharged from the hospital, both of which are prognosis-oriented factors for poor outcomes. The...
Heart Disease Prediction Using Data Mining Technique Data mining classification techniques is implemented to analyze the different kinds of heart based problems. This paper is aimed at developing a heart disease prediction system using data mining clustering techniques.. The health care system... M Nag...
Heart disease is the leading cause of death worldwide.Predicting heart disease is challenging because it requires substantial experience and knowledge.Several research studies have found that the diagnostic accuracy of heart disease is low.The coronary heart disorder determines the state that influences ...
Largest study on various researches of deaths shows that heart diseases have emerged as the most popular killer disease in world. About 25 percent of deaths between the 25-69 years of age group occur due to different heart related problems. According to world health organization, it is the fir...
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper,
N2 - Aim: The Framingham risk score (FRS) is one of the standard tools used to predict the incidence of coronary heart disease (CHD). No previous study has investigated its efficacy for a Japanese population cohort. The purpose of this study was to develop new coronary prediction algorithms ...
(NCEP) cholesterol categories with coronary heart disease (CHD) risk, to incorporate them into coronary prediction algorithms, and to compare the discrimination properties of this approach with other noncategorical prediction functions.This work was designed as a prospective, single-center study in the...
Das and Turkoglu22 have proposed an ANN ensemble-based predictive model for the prediction of heart disease. Similarly, Paul and Robin23 have used the adaptive fuzzy ensemble method for the prediction of heart disease. Likewise, Tomov et al.24 have introduced a deep neural network for heart ...
Heart disease is one the leading causes of death globally, making the early detection of it crucial. Emerging technologies such as machine learning and deep learning are now being actively used in biomedical care, healthcare, and disease prediction. The focus of this paper is on the prediction ...