New interpretable machine learning techniques and an application to stroke prediction in atrial fibrillation patients

Building interpretable and accurate models are attracting more and more interest in the machine learning community. In this thesis, we developed an interpretable machine learning algorithm called SBRL and we built an interpretable and statistically more accurate model for predicting strokes for patients in atrial fabrication (AF) who have not had a prior history of stroke and who are not taking anticoagulants. The first part of the thesis presents an interpretable machine learning algorithm that can be used as an alternative algorithm to the decision tree algorithm. Our algorithm builds an optimized rules list model from data by maximizing the posterior probability of a natural hierarchical generative model. It has the form of chained IF-THEN clauses which is simple for a human to follow and derive its prediction by hand. We developed two theoretical bounds for the algorithm.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Yang, Hongyu,Ph. D.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Cynthia Rudin.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/124095

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/124095