Integrative Approach to Cardiovascular Health Management

Abstract

Heart diseases remain a significant global health challenge, emphasizing the importance of early intervention and prevention. In addressing this issue, a mobile application was developed with four main components focusing on predicting the risk of becoming a heart patient, predicting the risk category of a heart patient, food item identification and dietary recommendation, and suggesting best rated nearby pharmacies to fulfill prescriptions. All these components include trained Artificial Intelligence models to assure a reliable performance within each component. In predicting the risk of becoming a heart patient, a trained logistic regression algorithm was used. As for predicting the risk category of a heart patient, a trained K-Nearest Neighbors algorithm was used. For the components of food item classification and suggesting best rated nearby pharmacies, YOLOv5 and TextBlob were utilized respectively. Results show that the accuracies of the trained models in the components are significant enough for mass scale utilization of all the four components of our mobile application in improving life standards of heart patients.