This method aims to develop a ML model to detect cardiac disease in its early stages. The different ML algorithms such as RF, KNN, LR, DT and SVM are used to attain the maximum accuracy. UCI repositories dataset was used for performing the experiments. In this study the accuracy of 80.33...
Heart Disease Prediction System is the system that helps to predict the heart disease mainly cardiovascular disease that includes Myocardial infractions. The importance of heart disease prediction system can be visualized from the fact that heart disease is one of the diseases that causes highest morta...
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 ...
Statistics of heart disease often include temporal characteristics, such as the history of the patient as well as variations over time. Effectively processing sequential data using ML approaches is challenging. Previous studies didn’t provide sufficient support for better patient outcomes. In this secti...
Retracted: An Effective Machine Learning-Based Model for an Early Heart Disease Prediction This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Co... BM International - 《Biomed Research International》...
Heart disease is a fatal human disease, rapidly increases globally in both developed and undeveloped countries and consequently, causes death. Normally, in this disease, the heart fails to supply a sufficient amount of blood to other parts of the body in
A voting classifier algorithm (ACD-VC) achieved the highest c-statistics among the six ML algorithms. The permutation feature importance technique enabled identification of the top predictors such as Barthel index, age, body mass index, duration of hospitalization, last hospitalization, renal disease,...
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,
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 ...
Background: Postoperative blood coagulation assessment of children with congenital heart disease (CHD) has been developed using a conventional statistical approach. In this study, the machine learning (ML) was used to predict postoperative blood coagulation function of children with CHD, and assess an...