Personal Data Mining: Solving a Mystery

In a previous post I described the way that I captured a year’s worth of personal data using my smartphone with the purpose of identifying trends with my immune system, which at times gave me perennial conjunctivitis and also swollen lymph nodes. Now it is time to analyze all of this data in hopes that some useful knowledge can be found.

I had to make a decision about which tools to use.

I had to make a decision about which tools to use. I used WEKA and also decided to give KNIME a try, so here is an example of a KNIME workflow :

I first use the file reader to read my year’s worth of life data, then an R Script is used for several data transformations. I then send the streams of data first to an R Script, which runs the FSelector package to which several feature selection algorithms (about 10 of them) are applied to get an understanding of what are the important features for the problem at hand.

Then another stream sends the data to an R node, which creates dummy variables and then sends the transformed features to a linear correlation node for further inspection.

A third stream (not shown) sends the data to 3 machine-learning algorithms (namely an SVM, Decision Tree and Random Forest) and the Scorer shows how each algorithm performed.

I first executed the FSelector node using 10-fold cross-validation because I wanted to get a first feel of the features that are important in identifying some patterns about my perennial conjunctivitis. 7 out of 10 of FSelector algorithms agreed that

1) Vitamin D3
2) Garlic
3) Yoghurt

appear to have the most predictive power. The problem is that at this point we do not know if any of the features actually help or aggravate my condition. However, the output of FSelector gives an idea as to which features should be looked at more closely.

Then the second stream was run, the one that sends the data to three machine-learning algorithms so that I could get a first feel of how the algorithms perform. All three algorithms gave an F-Score of around 59 – 62%.

By looking at the results some patterns appeared to arise (note the word “appeared”):

1) A rather large daily dose (>5200 IU) of Vitamin D3 appears to be associated with smaller incidences of conjunctivitis.
2) Garlic consumption appears to increase my conjunctivitis incidences.
3) Yoghurt consumption appears to increase my conjunctivitis incidences.

For Pattern (1) we need to be aware that Vitamin D3 dosage has a compounding effect so it is rather naive to think that boolean logic applies (see previous post for more).

Next I had to look at patterns (2) and (3). One of the things that I realized when searching the web for the effect of various nutrients in functions of the human body is the fact that you can find for any several entries that sometimes contradict each other. My very brief web search has found garlic and yoghurt to be “immune boosters.” Of course caution should be exercised in drawing any conclusions because of the way the data have been collected and also the problematic origin of the analysis. Moreover, I am not a doctor and I cannot possibly know whether garlic or yoghurt can aggravate an immune response in such a way.

I began taking Vitamin D3 and eliminating garlic and yoghurt from my diet. The result was that over a period of one month I stopped getting bouts of conjunctivitis and incidences of swollen lymph nodes. So has Vitamin D3 acted as an “immune response regulator” and garlic – yoghurt as “immune boosters”?

Although my bouts of conjunctivitis have ceased, I am not in any position to make any claims because there are a lot of uncontrolled variables:

– It could be a placebo effect.
– There may be unknown hidden variables that are important.
– Genetics.
– Environment.
– Variations in dosage and nutrient content.
– Interactions between nutrients.

There are lots of others that could not possibly be accounted for under these circumstances.

What I can say (and this is the reason for writing this post) is that analytics may help us to identify several patterns that may then be used to guide a sound knowledge discovery process. If people had the ability to collect data on a daily basis (see Quantified Self) and then analyze them on a massive scale, several unknown patterns that call for closer investigation could emerge.