<![CDATA[Early detection and diagnosis of a diseases will have a big impact on the medical field and help to prevent loss of life. This study begins by gathering information on myocardial infraction patients from hospitals and focuses on earlier diagnostics. In fact, the pre-processed, confirmed data from a qualified doctor is used for this research. Early prediction of myocardial infarction (MI) is proposed by many researchers. They have used Kaggle datasets that is not recent, and they work on post MI. We have proposed early myocardial infraction detection works on unsupervised datasets. To identify myocardial infraction, numerous machines learning supervised algorithms, including decision tree (DT), random forest (RF), are employed in the literature. In this study, we use the score tree algorithm (STA), which operates on an unsupervised dataset, to present a unique early MI prediction method.]]>
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