Assessing Heart Disease Risk in Patients with Anemia through Predictive Modeling

Abstract

Cardiovascular diseases are one of the leading causes of death worldwide and anemia is a common condition marked by low hemoglobin levels. Despite its prevalence anemia is often overlooked even though it plays a significant role in increasing the risk of heart disease. This study aims to develop a predictive model to assess the heart disease risk in patients with anemia. For this research, hematological and cardiovascular data were collected from 800 Bed Head Tickets at Jaffna Teaching Hospital, Sri Lanka. The data included patients with heart failure, myocardial infarction and those without heart disease. Manual data collection is difficult and takes time so MIMIC database was chosen as a backup. It is a publicly available dataset containing deidentified patient details and supported the initial stages of model implementation. After completing the manual data collection we applied multiple machine learning algorithms along with two ensemble approaches to train the data. The ensemble combining Logistic Regression and Support Vector Machine (SVM) achieved the highest accuracy of 79.38% and ROC-AUC of 82.02% with strong performance in other metrics. The proposed model demonstrates strong potential for clinical application by enabling early detection of high risk anemia patients. This will help facilitate timely medical check ups and reduce the risk of heart complications.