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- W3136135302 abstract "Abstract Cardiovascular is one of the most critical diseases that affect persons very abominably. Coronary artery diseases (CAD) are one of the categories of cardiovascular diseases that cause a high death rate. So it is imperative to control these death rates by developing an advanced model of machine learning through which diseases can be detected at the premature stage. Due to the lack of enough facilities for tools like angiography it has become a substantial challenge for the health care organization to detect such diseases. If tools exist, then these are known for being expensive and also have numerous side effects. The main goal of this research is to enhance the accuracy of existing models using optimization techniques with machine learning techniques. Alizadeh-Sani CAD dataset has been used which consists of a total of 303 records with 56 attributes. The proposed model reported following values of precision (0.92), recall (0.92), and accuracy (0.93). This proves the efficacy of employing the optimization techniques with machine learning algorithms." @default.
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- W3136135302 date "2021-03-01" @default.
- W3136135302 modified "2023-10-12" @default.
- W3136135302 title "Efficient Predictive Modelling for Classification of Coronary Artery Diseases Using Machine Learning Approach" @default.
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- W3136135302 doi "https://doi.org/10.1088/1757-899x/1099/1/012068" @default.
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