Application of Machine Learning Models to Predict Heart Disease

The backbone of this study is a dataset from a study of heart disease dataset. This hands-on workshop will dive into the basics of application of Machine Learning Algorithms to delve into the potential of building ML & DL Models from scratch to predict the presence or absence of heart diseases.

The backbone of this study is a dataset from a study of heart disease that has been open to the public for many years. The study collects various measurements on patient health and cardiovascular statistics, and of course makes patient identities anonymous. Developing a Machine Learning Predictive Model that could enhance the predictive power of not only historical patient health data but also with present and future patient health data, with less bias and variance in the model, is the need of the day. This hands-on workshop will help to deep dive into the basics of application of Machine Learning Algorithms to delve into the potential of building ML & DL Models from scratch to predict the presence or absence of heart diseases.

I have presented this Research as a Poster Presentation at the Australian & New Zealand Statistical Conference. This Research Project also made it to the Finals of the INFORMS Analytics Competition held by the Chicago Chapter. The goal of this Research Paper is to understand the application and the performance of various Machine Learning Models and understand how similar techniques could be potentially applied to related datasets from similar or different business domain.