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Home > Data > Machine Learning > Machine Learning for Healthcare Analytics Projects
Machine Learning for Healthcare Analytics Projects
Machine Learning for Healthcare Analytics Projects

Machine Learning for Healthcare Analytics Projects: Build smart AI applications using neural network methodologies across the healthcare vertical market

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Machine Learning for Healthcare Analytics Projects

Diabetes Onset Detection

The far-ranging developments in healthcare over the past few years have led to a huge collection of data that can be used for analysis. We can now easily predict the onset of various illnesses before they even happen, using a technology called neural networks. In this chapter, we are going to use a deep neural network and a grid search to predict the onset of diabetes for a set of patients. We will learn a lot about deep neural networks, the parameters that are used to optimize them, and how to choose the correct parameters for each.

We will cover the following topics in this chapter:

  • Detecting diabetes using a deep learning grid search
  • Introduction to the dataset
  • Building a Keras model
  • Performing a grid search using scikit-learn
  • Reducing overfitting using dropout regularization
  • Finding the optimal hyperparameters
  • Generating predictions using optimal...

Detecting diabetes using a grid search

We will be predicting diabetes on a of patients by using a deep learning algorithm, which we will optimize with a grid search to find the optimal hyperparameters. We are going to be doing this project in Jupyter Notebook, as follows:

  1. Start by opening up Command Prompt in Windows or Terminal in Linux systems. We will navigate to our project directory using the cd command.
  2. Our next step is to open the Jupyter Notebook by typing the following command:
jupyter notebook
Alternatively, you can use the jupyter lab command to open an instance of Jupyter Lab, which is just a better version of Notebook.
  1. Once the Notebook is open, we will rename the unnamed file to Deep Learning Grid Search.
  2. We will then import our packages using general import statements. We will print the version numbers, as shown in the following screenshot:

Keras has two options...

Introduction to the dataset

Our next step is to import the Pima Indians diabetes dataset, which contains the details of about 750 patients:

  1. The dataset that we need can be found at https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv. We can import it by using the following line:
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
  1. If we navigate to the preceding URL, we can see a lot of raw information. Once we have imported the dataset, we have to define column names. We will do this using the following lines of code:
names = ['n_pregnant', 'glucose_concentration', 'blood_pressure (mm Hg)', 'skin_thickness (mm)', 'serum_insulin (mu U/ml)', 'BMI', 'pedigree_function', 'age', 'class']

As can...

Building our Keras model

We'll now start building our Keras model, which is a deep learning algorithm:

  1. The first thing that we're going to do is import the necessary packages and layers. We will do that by running the following lines of code:
from sklearn.model_selection import GridSearchCV, KFold
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.optimizers import Adam

In the preceding code snippet , GridSearchCV is the function we will use to perform a grid search, and KFold will be used for performing the k-fold cross-validation. KerasClassifier is used as the wrapper. Adam is the optimizer that we will be using for this model; the rest of the functions are all general functions used to define the model:

  1. Let's start by defining the model. We will use a create_model() function, because...

Performing a grid search using scikit-learn

It's now time to prepare our grid search algorithm. We will follow a step-by-step process to make it easier to understand and execute:

  1. The first thing that we will do is copy the create_model() function, which we created in the Building a Keras model section, and paste it into a new cell, as shown in the following screenshot:
  1. Now, we will define a random seed through NumPy. This helps us to create results that are reproducible. We are also going to add random initialization of weights and random divisions of data into different groups. We will set a starting point so that we have the same initialization and the same divisions for all the data. This can be done by adding a few lines of code above the create_model() function, as shown in the following screenshot:
  1. Our next step is to initialize the KerasClassifier that we imported...

Reducing overfitting using dropout regularization

We will now use the information we gained in the Performing a grid search using scikit-learn section to optimize other aspects of our model. It looks like we might be overfitting the data a little bit, as we are getting better results on our training data than our testing data. We're now going to look at adding in dropout regularization:

  1. Our first step is to copy the code that is present in the grid search cell that we ran in the previous section, and paste it in a fresh cell. We will keep the general structure of the code and play around with some of the parameters present.
  1. We will then import the Dropout function from keras.layers using the following line:
from keras.layers import Dropout
  1. We will now convert the learning rate into a variable by defining it in the Adam optimizer code block. We will use learn_rate as...

Finding the optimal hyperparameters

We're now going to optimize the weight initialization that we're applying to the end of each of these neurons:

  1. To do this, we will first copy the code from the cell that we ran in the previous Reducing overfitting using dropout regularization section, and paste it into a new one. In this section, we won't be changing the general structure of the code; instead, we will be modifying some parameters and optimizing the search.
  2. We now know the best learn_rate and dropout_rate, so we are going to hardcode these and remove them. We are also going to remove the Dropout layers that we added in the previous section. We will modify the learning rate of the Adam optimizer to 0.001, as this is the best value that we found.
  3. Since we are trying to optimize the activation and init variables, we will define them in the create_model() function...

Optimizing the number of neurons

Let's now move on to tuning the number of neurons in each of these layers. Since we are following the same steps as the preceding sections, we will go through all these steps and do a recap with the code snippet at the end. So, let's get started with the following steps:

  1. We will start by copying the code from the cell used in the Finding the optimal hyperparameters section, and paste it in a new cell. In this new cell, we will play around with the number of neurons by modifying some of the variables.
  2. We will convert the total number of neurons present in each hidden layer into variables, such as neuron1 and neuron2. We will also define these variables in the create_model() function, so that they are called every time we execute it.
  3. We will also change the kernel_initializer and activation values to tanh and normal, since those were the...

Generating predictions using optimal hyperparameters

We know now some optimal hyperparameters for our grid search. We will use these to predict the onset of diabetes for the patients in our dataset. To do this, we will carry out the following steps:

  1. We will predict whether diabetes will occur for every example in the dataset by using the predict() function, as shown in the following code snippet:
# generate predictions with optimal hyperparameters
y_pred = grid.predict(X_standardized)
  1. We will then use the .shape command to see what the predictions look like. The following screenshot shows the output for this step:

From the preceding screenshot, we can see that there are 392 predictions with a numerical value for each.

  1. Let's print off the first five and see what they look like. We get the following output:
  1. We are now going to do a classification report and get an...

Summary

In this chapter, we built a deep neural network in Keras and we found the optimal hyperparameters using the scikit-learn grid search. We also learned how to optimize a network by tuning the hyperparameters. Note that the results that we get might not be the same for all of us, but as long as we get similar predictions, we can consider our model a success. When you start training on new data, or if you're trying to address a different problem with a different dataset, you will have to go through this process again. In this chapter, we also learned about deep learning and hyperparameter optimization and explored how to apply them to the network to predict the onset of diabetes on a huge dataset of patients.

In the next chapter, we will look at how to classify DNA using machine algorithms.

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Key benefits

  • Develop a range of healthcare analytics projects using real-world datasets
  • Implement key machine learning algorithms using a range of libraries from the Python ecosystem
  • Accomplish intermediate-to-complex tasks by building smart AI applications using neural network methodologies

Description

Machine Learning (ML) has changed the way organizations and individuals use data to improve the efficiency of a system. ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. Machine Learning for Healthcare Analytics Projects is packed with new approaches and methodologies for creating powerful solutions for healthcare analytics. This book will teach you how to implement key machine learning algorithms and walk you through their use cases by employing a range of libraries from the Python ecosystem. You will build five end-to-end projects to evaluate the efficiency of Artificial Intelligence (AI) applications for carrying out simple-to-complex healthcare analytics tasks. With each project, you will gain new insights, which will then help you handle healthcare data efficiently. As you make your way through the book, you will use ML to detect cancer in a set of patients using support vector machines (SVMs) and k-Nearest neighbors (KNN) models. In the final chapters, you will create a deep neural network in Keras to predict the onset of diabetes in a huge dataset of patients. You will also learn how to predict heart diseases using neural networks. By the end of this book, you will have learned how to address long-standing challenges, provide specialized solutions for how to deal with them, and carry out a range of cognitive tasks in the healthcare domain.

Who is this book for?

Machine Learning for Healthcare Analytics Projects is for data scientists, machine learning engineers, and healthcare professionals who want to implement machine learning algorithms to build smart AI applications. Basic knowledge of Python or any programming language is expected to get the most from this book.

What you will learn

  • Explore super imaging and natural language processing (NLP) to classify DNA sequencing
  • Detect cancer based on the cell information provided to the SVM
  • Apply supervised learning techniques to diagnose autism spectrum disorder (ASD)
  • Implement a deep learning grid and deep neural networks for detecting diabetes
  • Analyze data from blood pressure, heart rate, and cholesterol level tests using neural networks
  • Use ML algorithms to detect autistic disorders
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Table of Contents

6 Chapters
Breast Cancer Detection Chevron down icon Chevron up icon
Breast Cancer Detection
Objective of this project
Detecting breast cancer with SVM and KNN models
Training models
Summary
Diabetes Onset Detection Chevron down icon Chevron up icon
Diabetes Onset Detection
Detecting diabetes using a grid search
Introduction to the dataset
Building our Keras model
Performing a grid search using scikit-learn
Reducing overfitting using dropout regularization
Finding the optimal hyperparameters
Optimizing the number of neurons
Generating predictions using optimal hyperparameters
Summary
DNA Classification Chevron down icon Chevron up icon
DNA Classification
Classifying DNA sequences
Summary
Diagnosing Coronary Artery Disease Chevron down icon Chevron up icon
Diagnosing Coronary Artery Disease
The dataset
Fixing missing data
Splitting the dataset
Training the neural network
A comparison of categorical and binary problems
Summary
Autism Screening with Machine Learning Chevron down icon Chevron up icon
Autism Screening with Machine Learning
ASD screening using machine learning
Introducing the dataset
Splitting the dataset into training and testing datasets
Building the network
Testing the network
Summary
Another Book You May Enjoy Chevron down icon Chevron up icon
Another Book You May Enjoy
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  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela