Prediction and prevention of sexual dysfunction using a machine learning approach

Sexual dysfunction is pervasive in our society and is associated with a blend of biological, psychological, relational and contextual risk factors. In this project we will apply machine learning algorithms on a set of known risk factors of sexual dysfunction to predict whether a new patient suffers from sexual dysfunction and whether sexual dysfunction will improve based on specific patient treatment plans. This research work will set the foundation on building a artificial intelligent powered sexual dysfunction module of EarlyDetect, a psychiatric screening tool developed by Chokka Center for Integrative Health aimed to enhance accuracy and efficiency of the mental health screening process in Canada.

Faculty Supervisor:

Bo Cao

Student:

Yang Liu

Partner:

Chokka Center for Integrative Health

Discipline:

Education

Sector:

Medical devices

University:

Program: