Predictive Modelling of Heart Failure Risk and Survival

• 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.

Day 1: Machine Learning and Algorithm

• Overview of Artificial Intelligence, Machine Learning, and Deep Learning.
• Logistic Regression (LR), Decision Trees, Random Forest (RF), Support Vector Machines (SVM), and K-Nearest Neighbour (KNN).

Day 2: Bioconductor packages and Machine Learning applications

• R and Bioconductor packages installation
• Key applications of machine learning in predicting heart failure survival

Day 3: Biomedical Data analysis and Machine Learning

• Machine learning prediction model via Exploratory data analysis, data cleaning, Upsampling, Feature selection, Classification methods.
• Evaluation of model accuracy

• 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.