Applied ML in Medical Prognosis [Ref: TT10092]

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The demand for Machine Learning Engineers/AI specialists in the field of Medicine, Healthcare, and Public Health experiencing rapid growth.  

Improve your mastery with the Techtern Applied ML in Medical Prognosis and become job-ready

Problem Statement :

Being able to predict the future health of a patient is very critical information both in patient care and treatment strategies. Medical data features nonlinear relationships and this behavior has to be accounted for in building any model that helps predict future health states

Aim :

This project introduces you to the techniques for modeling patient health risks using statistical methods and a random forest predictor. You will learn to build risk models and survival estimators for heart disease using tree-based modeling techniques. You will use decision trees to model non-linear relationships in data that are commonly observed in medical data and apply them to predicting mortality rates in patients more accurately

Project Contents :

Dataset Information

Image pre-processing and Exploratory Data Analysis (EDA)

Modeling

Conclusion

What you will learn :

Build and Evaluate a Linear Risk model

Risk Models Using Tree-based Models

Survival Estimates that Varies with Time

Cox Proportional Hazards and Random Survival Forests

Dealing with class imbalance

Data augmentation techniques

Requirements:

Basic knowledge of Python required