Few of the supervised learning algorithms are used for the prediction of heart disease. It provides a quick and easy understanding of various prediction models in data mining and helps to find the best model for further work. 展开 DOI: 10.5120/ijca2017913868 被引量: 1 ...
Heart disease is one of the most critical human diseases in the world and affects human life very badly. In heart disease, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis of heart disease is important for heart failure ...
The paper investigates the powerful of hybridizing two computational intelligence methods viz., Gray Wolf Optimization (GWO) and Artificial Neural Networks (ANN) for prediction of heart disease. Gray wolf optimization is a global search method while gradient-based back propagation method is a local se...
[translate] aPrediction of Coronary Heart Disease Using Risk Factor Categories 冠状心脏病的预言使用风险因素类别 [translate] 英语翻译 日语翻译 韩语翻译 德语翻译 法语翻译 俄语翻译 阿拉伯语翻译 西班牙语翻译 葡萄牙语翻译 意大利语翻译 荷兰语翻译 瑞典语翻译 希腊语翻译 51La ...
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 ...
The levels of serum s-RNYs have been found significantly upregulated in patients with coronary heart disease (CHD) compared to age-matched CHD-free individuals. The present study aimed to examine the predictive value of serum s-RNYs for CHD events in the general male population. Within the ...
Sheena E Ramsay,Richard W Morris,Peter H Whincup, et al.Prediction of coronary heart disease risk by Framingham and SCORE risk assessments varies by socioeconomic position:results from a study in British men. European Journal of Cardiovascular Prevention & Rehabilitation . 2011...
Prediction of heart disease is crucial for healthcare since it may improve patient outcomes significantly when it is detected early and accurately. However, there are certain issues with adaptability, interpretability, and training speed in the existing prediction model. This work created a cutting-...
Heart Disease Prediction A Machine Learning model to predict Heart Disease. Context: This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this ...
This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this date. The "goal" field refers to the presence of heart disease in the patient. ...