Machine Learning for categorizing women’s health risks

This research project deals with categorizing women belonging to different developing countries into different health risk segments and sub segments and subsequently analyze patterns of diseases/infections in various geographical regions accordingly by using Optimized machine learning approach. XgBoost algorithm would be implemented to achieve this goal among other existing algorithms as this offers better model efficiency and performance. Efficient implementation of these programs, would not only improve health outcomes, but could save time and money for health organizations and practitioners. Furthermore, this platform could be used to create automated customized awareness programs for patients (for both male and female). This will aid policy makers in designing specific programs and policies to address women’s health in developing nations.

Faculty Supervisor:

Vijay Mago

Student:

Partner:

Furman University

Discipline:

Computer science

Sector:

Technology; Health and Related Sciences & Technology; Other

University:

Lakehead University

Program: