• Skip to primary navigation
  • Skip to content
  • Skip to footer
  • Career
  • Alumni
  • Employers
  • News

Fung Institute for Engineering LeadershipFung Institute for Engineering LeadershipFung Institute for Engineering Leadership

  • Master of Engineering
    • Master of Engineering Program
      • Engineering Departments
      • Leadership Development
      • Capstone Experience
      • Program Design
      • Learn More
      • How to Apply
  • Fung Fellowship
    • Fung Fellowship

      The Fung Fellowship is shaping a new generation of entrepreneurial leaders focused on transforming health and wellness.

      • Program Overview
    • The Fung Fellowship
    • Executive & Professional Education
  • Partners
    • Partners
    • Become a Partner
    • Propose a Project
    • Recruit a Student
  • Apply
  • About
  • Career
  • Alumni
  • News
Precision Healthcare Analytics Capstone team photo

Precision Healthcare Analytics

December 28, 2018 by

The team used machine learning to discover correlations between various lifestyle behaviors and two chronic illness: depression and cognitive dysfunction. They found a way to identify these diseases early on, and to provide recommendations for lifestyle changes in order to lessen their debilitating effects.

Team: Herbreteau Eléonore, Papanikolaou Vasileios, Ramesh Kapilesh, Shi Yiyu, Vyas Arpit
Advisor: Dr. Anil Aswani

Chronic diseases are the leading cause of death and disabilities in the United States. About half of all adults have had one or more chronic health conditions and these account for 86% of the US healthcare expenses.

Our project aims to identify correlations between people’s lifestyle behaviors and two chronic diseases, namely mental depression and cognitive dysfunction using machine learning approaches. We utilized advanced analytical tools and machine learning algorithms to accomplish this. The result is early identification of these chronic diseases and lifestyle change recommendation to minimize people’s exposure to them.

Example of most correlated features for depression:

  1. Smoking
  2. Moderate recreational activities
  3. Type of work done last week
  4. Minutes of sedentary activities
  5. Moderate work activities

Algorithms used:

  • Logistic Regression
  • Decision Tree
  • Random Forest
  • Clustering
  • SVM
  • Gradient Boosting

← View all Capstone Projects

Fung Institute For Engineering Leadership
Shires Hall
2451 Ridge Road Berkeley, CA 94709

Mudd Hall
1798 Scenic Avenue Berkeley, CA 94709

(510) 642-0633
funginstitute@berkeley.edu

Explore

  • Programs
  • Partners
  • Apply
  • Feedback

Experience

  • About
  • Career
  • Alumni
  • News
  • Donate

Connect

Copyright © 2022 Accessibility • Nondiscrimination • Privacy • Sitemap

berkeley_engineering

uc-berkeley

Copyright © 2022 Accessibility • Nondiscrimination • Privacy • Sitemap

berkeley_engineering

uc-berkeley

Prospective MEng Students

Sign up for our mailing list to receive program news and updates including information sessions, class visits and opportunities to connect with an admissions advisor.