Predictive Modelling of Heart Failure Risk and Survival

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).

The goal is to forecast the survival of patients with heart failure and determine the key risk factors by utilizing classification techniques on extensive datasets of medical records belonging to heart failure patients.

• To characterize data and clarify the connection between one dependent binary variable and one or more independent variables that can be nominal, ordinal, interval, or ratio-level.
• To develop a model that forecasts the target variable’s value by identifying straightforward decision rules derived from the features of the data.
• To identify the initial indicators of heart failure progression, allowing for prompt intervention and the possibility of enhancing patient outcomes.

• Healthcare professionals with a basic understanding of R programming.
• Data scientists interested in medical data applications.
• Students and researchers in medical informatics or related fields.

• Proficiency in machine learning applications in healthcare.
• Ability to build and assess predictive models for heart failure.
• Skills to identify and analyze key risk factors in patient data.