Application of Machine Learning in Heart Failure Prediction

Abstract

Cardiovascular diseases (CVDs) are the primary cause of death worldwide, causing an estimated 17 million deaths each year, and are characterized by myocardial infarction and heart failure. Heart failure occurs when the heart is incapable of adequately pumping blood to satisfy the body's demands. It typically stems from conditions such as diabetes, hypertension, or other cardiac disorders. Early detection and management are crucial for individuals with cardiovascular disease or those at an elevated risk of developing cardiovascular issues due to factors such as hypertension, diabetes, hyperlipidemia, or pre-existing medical conditions. In this regard, utilizing a machine learning model can offer significant benefits. This research paper will utilize machine learning models to document the symptoms, physical attributes, and clinical laboratory test results of patients. Consequently, through the analysis of this patient information, biostatistical analyses can be conducted, enabling the detection of patterns and correlations that may elude medical practitioners. The research will benefit the research on cardiovascular diseases.