About Workshop
Machine learning can help forecast a patient's chances of surviving heart failure by examining large datasets that comprise demographics, clinical measurements, and imaging results. This analysis identifies patterns and crucial risk factors that can reliably predict a patient's survival rate, enabling healthcare providers to effectively stratify patients and make informed treatment choices tailored to individual risk profiles.
In this workshop, a significant application of machine learning has been illustrated by forecasting heart failure survival through the analysis of a patient's medical history, laboratory tests, and imaging results. Machine learning models are capable of identifying heart failure patients at high risk who may necessitate closer monitoring and treatment interventions. Various machine learning methods have been utilized for predicting heart failure survival, including Logistic Regression (LR), Decision Trees, Random Forest (RF), Support Vector Machines (SVM), and K-Nearest Neighbour (KNN). Utilizing multiple ML classifiers can improve the precision of predicting cardiovascular disease (CVD) risk. Additional research in this field can help enhance CVD forecasting and diagnosis. Machine learning serves as a powerful tool for predicting heart disease (HD).
