Acute coronary syndromes are among main complains for patients presenting to emergency department. Risk classification systems are used to classify patients to appropriate risks and help physicians manage diagnosis strategies and treatments. Purpose of this study is to develop a clinical decision support system for patients presenting to emergency department with the help of statistical machine learning.

Acute coronary syndromes are among main complains for patients presenting to emergency department and create a burden to emergency departments and hospitals. Risk classification systems were developed and used to classify patients to appropriate risk groups. According to risk classification, different diagnosis and treatment modalities can be used and help physicians manage diagnosis strategies and treatments. Purpose of this study is to develop a clinical decision support system for patients presenting to emergency department with the help of statistical machine learning.

Inclusion Criteria:

• Presenting to emergency department with acute chest pain consistent with acute coronary syndrome

Exclusion Criteria:

• Patients with less than 18 years old
• Patients presenting with ST-segment elevation myocardial infarction
• Patients presenting with other causes of acute chest pain (pneumothorax, pulmonary -embolism, aortic dissection
• Decline to participate in study

Patients presenting to emergency deparmtent with acute chest pain consistent with acute coronary syndrome will be included for analysis.