Published Paper Details:

As one of the most common causes of mortality worldwide, cardiac infarction is currently the condition thatwilltakeyourlifethemostquickly.ThemortalitytollmaybereducedbyearlydetectionofCardiacviolations.Recent years have seen an increase in the difficulty of predicting cardiac arrests. According to recent data, aheart-relateddeathoccursalmostonceperminute.Datascienceisvitalintherealmofhealthcaresincethereis a lot of data to analyse. Different machine learning methods may be used to anticipate cardiac violationsbased on what has been seen. Based on a person's cholesterol, blood pressure, age, sex, etc., these machinelearningalgorithmapproacheswereutilisedtodeterminehowprobableitwasthatapersonwillhaveaCardiacinfraction.Ourstudyreliedontwotypesofdata.UCI'smachinelearningrepositoryprovideduswiththefirstCardiac infraction observed dataset. For each of the 303 records, 14 distinct properties are included (13featuresandonetarget).UsingtheKagglewebsite,wefoundaseconddatasetofpatientrecordinstanceswith11 characteristics and a single target.