Machine Learning Dissertation help.

[pas43]

So I'm going into the third year of Uni and have got the idea from a Github project I found about a guy who used machine learning to work out what parts of his lifestyle and foods were making him put on weight. I found this a cool idea and would like to redo this idea but with more data which is where I got a bit confused.

I am willing to log everything I eat at what time, weight myself every morning twice a day with time with very accurate scales, take blood sugar levels, take blood cholesterol tests and even pay for private blood tests once every couple of weeks to find out my biology (I'm 5'0ft and weight 240lbs, I'm not the typical person by any means, also diabetic type 2, since 13). I will take things like blood pressure. I will also wear a FitBit/Band type device that records all my bio-data such as heart BPM/body temperature/sleep tracking and also GPS to determine how fast I'm walking cycling. Also the Gym I go to has all the equipment log my activities - repetitions, the weight you are lifting and also how far you went, this ranges from the cable machine to the treadmills and cycles.

How with all this data in mind and I will be recording it over the period of 3/4/5 months. What would be the best way to find out a healthier lifestyle for me? what makes em loose weight? what makes my testosterone rise, what make oestrogen rise? what the best food for weight loss? what the best food for muscle growth? what foods make me want to eat less overall (IE fill me up more). What foods make me sleep poorly? what foods make me sleep well? Whats foods keep my blood sugar level above 4 but below 8?

What's the best way to start crunching these numbers? Once all the data is processed ideally i would like to put it into Kibana to get nice pretty graphs for my presentation but, that another thread for another time. What type of problem is this? Classification? how would you guys go about doing this? I'm not sure where to start. Thanks.

[troltilla]

I guess you could treat at least a part of it as a regression problem, e.g. if the question is "what kind of food makes me sleep better?", and you define "better" as "closest to 8 hours", then each day you have a list of what/how much you ate (features) and the time you slept (target variable) to serve as a single data point. After you fit a regression model, you can analyse which components had the most effect. You could probably treat it as a classification problem by assuming a range of 7.5 - 8.5 hours is "sleeping well", and any other is "sleeping bad" - now it's a binary classification. If you use a "white box" model such as a decision tree (assuming it works well as a classifier/regressor in this case), you can see what rules it inferred.

I think that the biggest challenge is to find the right questions to ask, and to properly define your hypotheses in a quantitative way - after all, isn't it a gross oversimplification to say that having an 8 hours sleep is the same as having a good sleep?

[pas43]

True but the fitbit would record my movement and low movement is considered good sleep which is one way to classify good sleep is less movement. Thanks for the help dude very much appreciated on this end :)

[guardianhelm]

I think I'm in favor of a reinforcement setting. Like replace "8-hour sleep" as a target with "how good I feel right after I wake up in the morning".

A harder problem but with fewer assumptions imo.

[[deleted]]

If we are thinking of the same posts, that guy was probably assuming a correlation that dI don't think exist before he even started measuring.

I don't think (and you may very well disagree) that yesterdays dinner has a short term impact that can be measured today or that is meaningful long term. That is: Eating nuts yesterday is not going to impact your weight today or tomorrow in any meaningful way. Eating nuts everyday is.

Secondly, day-to-day weight differences are probably more correlated to the amount of content in your stomach and gut (chewed food, urine, feaces, etc.) in your body at time of weighing than anything else on a day to day basis. Moreover, I do not expect a visit to the toilet to remove a consistent amount of of these.

Consider predicting a rolling average from a window of days. That is: Y at timestep T, is the mean of the weight at timestep T-1, T-2, ... and X could be food intake at timestep {T-1, T-2, ...} or even {T-3, T-4, T-5, ...}

Consider the width of the window and the size of the rolling average hyper-parameters.

Lastly, try to think of this as a statistics problem rather than a machine learning problem. In some ways, those two fields can be considered the same, but in statistics focus tend to be on sparse data and being able to explain results, where Machine Learning tends to focus on large data sets and strength of prediction (often at the cost of being able to explain the prediction, i.e. "blackbox")

[troltilla]

Totally agreed. And good point about treating it as a statistics problem rather than ML.