TracyD
Active Member
For me, the answer is no.
Here's a blog post about my many, failed efforts to create a statistical model to predict my daily health using tracker data.
And here's the gist:
How well can the tracker data that’s available to me upon waking foretell my health status, as a person with ME/CFS (PWME), for the rest of the day? Over the past few years, I have applied a plethora of machine learning techniques to answer this question. The goal was to create an app to automatically predict PWME’s daily health each morning.
The result is disappointing: the best method will accurately predict whether my daily health will be ‘bad’ or ‘not bad’ only 57 percent of the time. Flipping a coin would result in 50 percent accuracy, so this is an improvement of only 7 percent.
I still believe that resting heart rate (RHR) and heart rate variability (HRV) generally change along with my health status, so what went wrong? My observation is that many other factors also influence these measurements, masking the relationship that I’m trying to model. The good-ish news is that a person likely can do a better job of taking these contingencies into account, making this a case when human learning probably outperforms machine learning.
Here's a blog post about my many, failed efforts to create a statistical model to predict my daily health using tracker data.
And here's the gist:
How well can the tracker data that’s available to me upon waking foretell my health status, as a person with ME/CFS (PWME), for the rest of the day? Over the past few years, I have applied a plethora of machine learning techniques to answer this question. The goal was to create an app to automatically predict PWME’s daily health each morning.
The result is disappointing: the best method will accurately predict whether my daily health will be ‘bad’ or ‘not bad’ only 57 percent of the time. Flipping a coin would result in 50 percent accuracy, so this is an improvement of only 7 percent.
I still believe that resting heart rate (RHR) and heart rate variability (HRV) generally change along with my health status, so what went wrong? My observation is that many other factors also influence these measurements, masking the relationship that I’m trying to model. The good-ish news is that a person likely can do a better job of taking these contingencies into account, making this a case when human learning probably outperforms machine learning.
![ME/CFS lecture | Dr Nancy Klimas | 2011 [Chronic Fatigue Syndrome / SEID / New Zealand] - YouTube](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
