A Comparative Analysis of Machine Learning Algorithms for Clinical Decision Support in Early Heart Failure Mortality Prediction

This project presents a comparative analysis of machine learning algorithms, including Random Forest, for clinical decision support in early heart failure mortality prediction. The system utilizes sensors such as a heart beat sensor, SpO2 sensor, temperature sensor, and respiratory sensor to continuously monitor vital health parameters in real time. The collected data is processed and analyzed using machine learning algorithms to evaluate the patient’s condition and predict the risk of heart failure. Among the algorithms, Random Forest is used for its high accuracy and reliable performance in classification. Based on this prediction, the system provides appropriate medical suggestions or medicine recommendations to assist healthcare decisions. An LCD display is used to show real-time readings and system outputs. By comparing different machine learning models, the system identifies the most accurate approach for prediction. This method enables early diagnosis, improves treatment decisions, and enhances overall patient monitoring in modern healthcare systems