Machine Learning Approaches for Predicting Heart Failure from Echocardiography Data

This study examines how to use ultrasound data to predict cardiac failure using a deductive methodology, interpretive theoretical framework, and a descriptive design of research. The foundation of the investigation is secondary information collecting from several healthcare organizations. Clinical factors including age, medical history, and test results are collected from echocardiography and combined with properties like the fraction of ejection, thickness of the walls, and valvular function. The main predictive tool utilizes the linear regression method, chosen for its clinical applicability and comprehension. Accuracy, sensitivity, particularity, and AUC-ROC measures are used to assess the algorithm's performance, giving a thorough evaluation of its discriminatory capacity. Results show a strong relationship between certain echocardiogram features and the risk of heart failure. Integrating clinical data improves predictive accuracy and provides a more complete picture of the condition of the patient. The main factors influencing heart failure prediction are clarified through the examination of coefficients with feature significance scores. The study's conclusions are clinically applicable and give healthcare professionals useful information for prompt action and individualized patient treatment. Throughout the investigation process, ethical issues including data protection and informed consent are carefully addressed. By offering a structured framework for using ultrasonography data in coronary artery disease prediction, this research advances the developing field of coronary medicine. Given the context of managing heart failure, it highlights the possibility for improved managerial choices and patient satisfaction.