Predicting Diabetes using Logistic Regression with TensorFlow.js

We have the data, let get familiar with it!ExplorationWhile tfjs-vis is nice and well integrated with TensorFlow.

js, it lacks (at the time of this writing) a ton of features you might need — overlay plots, color changes, scale customization, etc.

That’s why we’ll use Plotly’s Javascript library to make some beautiful plots for our data exploration.

Let’s have a look at the distribution of healthy vs diabetic people:Little above 65% of the patients in our dataset are healthy.

That means that our model should be more accurate than 65% of the time, to be any good.

Next up — the insulin levels:Note that there is a big overlap between the two distributions.

Also, we have a lot of 0s in the dataset.

Seems like we have a lot of missing values.

NaNs are replaced with 0s.

Another important one is the glucose levels after the test:While there is some overlap, this test seems like it separates the healthy from diabetic patients pretty well.

Let’s have a look at the age:Generally speaking, it seems like older people are more likely to have diabetes.

Maybe we should take a look at the relationship between age and glucose levels:The combination of those two seems to separate healthy and diabetic patients very well.

That might do wonders for our model.

Another combination you might want to try is the skin thickness vs BMI:Yep, this one is horrible and doesn’t tell us much :)PreprocessingCurrently, our data sits in an array of objects.

Unfortunately, TensorFlow doesn’t work well with those.

Luckily, there is the tfjs-data package.

We’re going to create a Dataset from our CSV file and use it to train our model with the createDatasets() function:The features parameter specifies which columns are in the dataset.

testSize is the fraction of the data that is going to be used for testing.

batchSize controls the number of data points when the dataset is split into chunks (batches).

Let’s start by extracting the features from the data:We’re replacing missing values in our features with 0s.

You might try to train your model without this step and see what happens?Let’s prepare the labels:Here’s the definition of oneHot:One-hot encoding turns categorical variables (healthy — 0 and diabetic — 1) into an array where 1 corresponds to the position of the category and all other variables are 0.

Here are some examples:1; // diabetic =>[0, 1];and healthy:0; // healthy =>[1, 0];Let’s create a Dataset from our data:Note that we also shuffle the data with a seed of 42 :)Finally, let’s split the data into training and validation datasets:We use take to create the training dataset, skip to omit the training examples for the validation dataset and finally, split the data into chunks using batch.

Additionally, we return data for testing our model (more on this later).

Logistic RegressionLogistic Regression (contrary to its name) allows you to get binary (yes/no) answers from your data.

Moreover, it gives you the probability for each answer.

Questions like:Is this email spam?Should I ask my boss for a higher salary?Does this patient have diabetes?Is this person a real friend?Does my partner cheat on me?Do I cheat on my partner?Do you get where I am getting at?are answerable using Logistic Regression if sufficient data is available and you’re lucky enough to believe there are answers to all of these?But I digress, let’s have a look at the mathematical formulation of the Logistic Regression.

First, let’s start with the Linear Model:where x is the data we’re going to use to train our model, b1​ controls the slope and b0​ the interception point with the y axis.

We’re going to use the softmax function to get probabilities out of the Linear Model and obtain a generalized model of Logistic Regression.

Softmax Regression allows us to create a model with more than 2 output classes (binary response):where b1​ defines the steepness of the curve and b0​ moves the curve left and right.

We want to use our data X and some training magic to learn the parameters b1​ and b0​.

Let’s use TensorFlow.

js for that!Note that this model will give us a probabilistic answer instead of just a binary response.

You might decide to ignore a prediction if the model is not sure about it — e.


below 80%.

Predicting diabetesLet’s put the theory into practice by building a model into TensorFlow.

js and predict the outcome for a patient.

The modelRemember that the key to building a Logistic Regression model was the Linear Model and applying a softmax function to it:Note that we have 2 outputs because of the one-hot encoding and dynamic input count, based on the features we’ve chosen to train the model.

Yes, it is that easy to build a Logistic Regression model in TensorFlow.


The next step is to compile the model:The training process of our model consists of minimizing the loss function.

This gets done by the Adam optimizer we’re providing.

Note that we’re providing a learning rate of 0.


The learning rate is known as a hyperparameter since it is a parameter you provide for your model to use.

It controls how much each new update should “override” what your model already knows.

Choosing the “correct” learning rate is somewhat of voodoo magic.

We’re using Cross-Entropy loss (known as log loss) to evaluate how well our model is doing.

It (harshly) penalizes wrong answers given from classification models, based on the probabilities they give for each class.

Here is the definition:where C is the number of classes, y is a binary indicator if the class label is the correct classification for the observation and p is the predicted probability that o is of class c.

Note that we request from TensorFlow to record the accuracy metrics.

TrainingLet’s use fitDataset to train our model using the training and validation datasets we’ve prepared:We train our model for 100 epochs (number of times the whole training set is shown to the model) and record the training logs for visualization using the onEpochEndcallback.

We’re going to wrap all of this into a function called trainLogisticRegressionwhich is defined as:EvaluationLet’s use everything we’ve built so far to evaluate how well our model is doing:Note that we only use the glucose levels for training our model.

Here are the results:Not good at all.

Our model performs worse than a dummy that predicts healthy 65% of the time.

Also, the loss never really starts dropping.

Let’s try with more data:Much better, the loss value is reduced significantly during training, and we obtain about 79% accuracy on the validation set.

Let’s take a closer look at the classification performance with a confusion matrix:The confusion matrix can be obtained using the model predictions and test set:Even though our model might’ve obtained better accuracy, the results are still horrible.

Being healthy is vastly overpredicted compared to having diabetes.

What if we try with a more complex model:Here is the confusion matrix for this model:We’ll not look into this model for now, but note that we obtain much better results by increasing the complexity of the model.

ConclusionCongratulations! You built and trained not one, but a couple of models, including Logistic Regression, that predicts whether or not a patient has Diabetes.

You’ve also met the real-world — processing data, training and building models, are hard things to do.

Moreover, not everything is predictable, no matter how many data points you have.

Run the complete source code for this tutorial right in your browser:That said, there are ways to improve the process of building and training models.

We know that using some techniques is better than others, in a certain context.

Well, Machine Learning is nuanced :)ReferencesLogistic Regression by Dr.

Saed SayadA Gentle Introduction to TensorFlow.

jsOriginally published at https://www.



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