Heart Disease
Acute myocardial infarction (AMI) is the main cause of death in developed and developing countries. AMI is a serious medical problem that necessitates hospitalization and sometimes results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Many studies have been conducted on the prognosis of AMI with hemogram parameters. However, no study has investigated potential hemogram parameters for the diagnosis of AMI using an interpretable artificial intelligence-based clinical approach.
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Abstract- Cardiovascular diseases (CVDs) remain a sig- nificant global health challenge, emphasizing the critical need for accurate predictive models to address early detec- tion and intervention. This study presents a comprehensive framework for heart disease prediction using advanced ma- chine learning techniques.
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This heart disease dataset is curated by combining 3 popular heart disease datasets. The first dataset (Collected from Kaggle) contains 70000 records with 11 independent features which makes it the largest heart disease dataset available so far for research purposes. These data were collected at the moment of medical examination and information given by the patient. Second and third datasets contain 303 and 293 intstances respectively with 13 common features. The three datasets used for its curation are:
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Cardio Data (Kaggle Dataset)
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This heart disease dataset is curated by combining 5 popular heart disease datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 common features which makes it the largest heart disease dataset available so far for research purposes. The five datasets used for its curation are:
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