Forecasting patient survival in cases of heart failure and determining the key risk factors using Machine Learning (ML)
Harness Machine Learning to Predict Heart Failure Survival and Improve Patient Outcomes
Virtual (Google Meet)
Mentor Based
Moderate
3 Days (1.5 hours per day)
2 -April -2025
10:00 AM IST
About
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).
Aim
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.
Workshop Objectives
- 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.
Workshop Structure
Day 1: Introduction to 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
Participant’s Eligibility
- 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.
Important Dates
Registration Ends
2025-04-02
Indian Standard Timing 9:00 AM
Workshop Dates
2025-04-02 to 2025-04-04
Indian Standard Timing 10:00 AM
Workshop Outcomes
- 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.
Mentor Profile
Designation: Professor and Head
Affiliation: Shalom New Life College, Bengaluru, Karnataka
Dr. Md Afroz Alam is a Professor and Head in the Department of Bioinformatics, at Shalom New Life College, Bengaluru, Karnataka. He received his Ph.D. Degree in Bioinformatics from Jaypee University of Information Technology, Solan, Himachal Pradesh in 2009. Then he has worked as Assistant Professor, Head and Program Coordinator in the Department of Bioinformatics at Karunya Institute of Technology and Sciences (Deemed University) for 11 Years. He is having 14 years of teaching and research experience in the field of Bioinformatics. His area of expertise includes: Computer Aided Drug Design, Molecular Modeling and Simulation, QSAR and Pharmacophore modeling, Biostatistics, R programming, Unix and Linux. He is the author of 24 research articles, 2 Book Chapter, recipient of short-term research grants, workshop grants, and National Youth leader award by Ministry of Youth Affairs and Sports under the National Service Scheme, Government of India.
Fee Structure
Student
INR. 1399
USD. 50
Ph.D. Scholar / Researcher
INR. 1699
USD. 55
Academician / Faculty
INR. 2199
USD. 60
Industry Professional
INR. 2699
USD. 85
List of Currencies
Key Takeaways
- Access to Live Lectures
- Access to Recorded Sessions
- e-Certificate
- Query Solving Post Workshop
Future Career Prospects
- Biomedical Data Scientist
- Clinical Data Analyst
- Health Informatics Specialist
- Machine Learning Engineer in Healthcare
- Research Scientist in Cardiology
- Healthcare Technology Consultant
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na
Contents were excellent