Statistical Learning for Heart Disease Diagnosis
Author’s Note (October 2020): In preparation for work on my published research paper Ada-WHIPS: explaining AdaBoost classification with applications in the health sciences, I wanted to develop some familiarity with medical statistics and diagnostic tools. I decided to take a fairly well-known medical data set and try something different. Instead of performing a straightforward classification model training as would be typical, I took a deep exploratory dive. My intention was to see if I could tease out some intuition as to what were the predictive patterns in the data. The exercise evolved into a potential approach to developing a statistical diagnostic tool, for similarly distributed data. My original report is reproduced below.
Freelancing In Paradise
I took a career break while I was still living in the tropics and had the chance to do some freelance work in between SCUBA dives. One of my first gigs was to help a client to evaluate their sales target setting process. Their team was split into two groups and some rumours were circulating that one group had their targets set too easy. I was able to recommend some improvements based on some very simple statistical tests.
Be Inclusive, Be Change
Recently, I had the opportunity to work with B-Change.org (a.k.a. Be-Inclusive.org) to clarify and focus strategy and positioning for their mobile app. B-Change strives to help marginalized groups, to feel more connected. Their app was intended to be a relaunch of a community web-site that served the Southeast Asian LGBT and HIV-positive youth community by offering them information about service providers who are more sympathetic to their specific needs. The app helped B-Change to deliver a much more socially connected experience with a renewed focus on user genenarated content but they were struggling with user adoption and traction.