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Automating fetal heart monitor using machine learning
1. Automating Fetus Health
Monitor Using Machine
Learning
Md. Tamjid Rayhan
Department of Electrical and Electronic Engineering
University of Chittagong
Session : 2012 - 2013
1
2. Outline of this seminar
• How I started my journey in Machine Learning and what plan do I
suggest for the newbies?
• How did I approach a Biomedical Engineering problem using machine
learning?
• Question and Answer session.
2
3. Machine learning: Beginner to Professional
• Which tutorials, programming languages, books and blogs suits the
beginners best?
• Do I need GPU’s , TPU’s, Cloud computing resources? If so, can I get
them for free?
• Where do I get my data for free?
• How do I know whether I have become a Pro in Machine Learning?
3
4. Where to Start?
• C /C++ / JAVA / MATLAB / R/ Python?
• There’s a github repo that can help you decide
• https://github.com/josephmisiti/awesome-machine-learning
4
5. Advantages of python
• Easy to learn
• Easy to find materials
• Easy to write code in python
• Enormous community who are very supportive
• There is a library / framework of python for almost any ML task
• Python can also be used for Web and GUI development
5
6. How to start learning python?
https://python.maateen.me/
6
9. Which IDE should you use?
• Here’s a link to all available options. Find one that suits you best:
• https://wiki.python.org/moin/PythonEditors
• I use iPython/Jupyter notebooks for data analysis and model
development
• I currently use VS code for my python development
• I started coding in the python IDLE that comes built in with python
installation.
9
10. Data structure and algorithms
Grokking Algorithms Introduction to algorithms
10
11. Machine learning is all about MATH!!!
• This is the truth , whether you are happy about it or sad about it!
• Do I need to be a math genius to start learning Machine Learning. Well the
answer is no!
• The prerequisite maths of learning machine learning is very simple: Matrix
multiplication, vector dot product, differentiation, mean, variance,
histogram.
• You need to love math though! Because each and everything you learn
about will involve math!
11
13. Essential Kaggle Micro-courses!
1. Python 5. Deep Learning
2. Pandas 6. Feature engineering
3. Intro to Machine Learning 7. Data visualization
4. Intermediate Machine Learning 8. Intro to SQL
13
14. Outcome of Kaggle Micro courses
Python Library Introduction Machine learning introduction
Pandas Your first ML Project
Scikit-learn Your first ML competition
Tensorflow Your first DL project
Matplotlib and Seaborn Data collection using SQL
14
15. The signature Machine Learning Course
https://www.coursera.org/learn/machine-learning
15
16. Outcome of “Machine Learning” course
Statistics Machine Learning Algorithms
Linear and Logistic Regression Neural Networks implementation
Concept of cost function and gradient
descent
SVM implementation
Concept of bias and Variance K-Means clustering implementation
Performance Metrics of an ML algorithm Recommender system implementation
16
17. Do I need GPU or TPU?
• No, instead use the free online virtual runtimes provided by Kaggle
and Google Colab
• In Google Colab you get a runtime with 12GB RAM and you have
access to GPU and TPU. Also you can mount your google drive as
permanent storage.
• In Kaggle you can upload your own dataset , do analysis on it using
their free runtime.
17
18. Where to get the data from?
• Kaggle competitions and datasets
• UCI Machine learning repository
• Physionet.org
• Google Dataset search
• Web scraping
• Retrieving data from relational databases using SQL
• Collecting and Building your own dataset
• Collecting data from existing products and services
18
19. What are the best books to learn Machine
learning?
Hands on Machine Learning using
scikit-learn and tensorflow
Deep Learning – Ian Goodfellow
https://www.deeplearningbook.org/
19
20. Dive into deep learning!
https://www.coursera.org/specializations/deep-learning
20
21. Miscellenius Resources
• Google crash course on machine learning
• https://developers.google.com/machine-learning/crash-course/
• This blog is for computer vision enthusiasts
• https://www.pyimagesearch.com/
• Subscribe the newsletter to deeplearning.ai
• https://www.deeplearning.ai/thebatch/
• Having difficulty setting up your GPU?
• https://course.fast.ai/#using-a-gpu
21
22. End to End learning vs. multiple stage
learning
22
23. Small data vs Big data
https://www.industryweek.com/technology-and-iiot/digital-tools/article/21122846/making-
ai-work-with-small-data
• Unlike consumer Internet companies, which have data from billions of
users to train powerful AI models, collecting massive training sets in
manufacturing is often not feasible.
• In a recent MAPI survey, 58% of research respondents reported that the
most significant barrier to deployment of AI solutions pertained to a lack of
data resources.
• Synthetic data generation, Transfer learning, Self-supervised learning, few-
shot learning, One-shot learning, anomaly detection, Human-in-the-loop
23
24. Solving a problem using Machine Learning
• Why did I choose to work on automating Fetus Health Monitor?
• Why Machine Learning is needed to solve this problem?
• What were my results? Did I answer my research questions?
• Is my work ready to get implemented in a hospital yet? If no, why
not?
24
27. Cardiotocography features
Feature Definition Ideal Value
Baseline Heart rate at rest 110 – 160 beats per minute
Variability Fluctuations from the baseline > 6 beats per minute
Acceleration
Abrupt increase from baseline of
15 beats/min that lasts for 15
seconds
Must be present in a healthy fetus
Deceleration
Decrease from baseline of 15
beats/min that lasts for 15 seconds
Decelerations are non- reassuring,
Should not be present in healthy
fetus
27
30. Similar Problems
• Detection of heart disease from ECG data
• Predicting epileptic seizure from analyzing EEG data
• Detecting Pneumonia from Chest X-ray Images
30
31. Objectives
• Research question 1: Is it possible to predict fetus health
automatically from cardiotocography data?
• Research question 2: If it is possible to predict fetus health
automatically from the Cardiotocography data, then how accurate is
that model in predicting fetus health?
• Research question 3: If it is possible to predict fetus health
automatically from the Cardiotocography data, then which features of
cardiotocography data are most important in predicting fetus health?
31
33. Model Development
Choose algorithm
Algorithm
applicability by
drawing learning
curve
Tune
Hyperparameters
from validation
curves
Train model
Calculate
performance metrics,
draw confusion
matrix and ROC
Compare the
performance of all
built models
33
34. Learning Curve for SVM
Learning curve for SVM with Linear kernel Learning curve for SVM with Gaussian kernel
34
35. Comparisn between built models
Model Description
Sensitivity
(Pathologic)
Sensitivity
(Suspected)
Precision (Normal) Accuracy
Logistic regression with
selected features
Good(0.946) bad(0.767) Excellent(0.976) Good(0.812)
Logistic regression with
all features
Excellent
(0.973)
Good(0.86) Excellent(0.988) Average (0.804)
Random forest with
selected features
Good (0.892) Good(0.837) Excellent( .974) Excellent( 0.953)
Random forest with all
features
Good(0.892) Good(0.884) Excellent( .98) Excellent( 0.953)
SVM with selected
features
Good(0.919) Good(0.837) Excellent(0.992) Good(0.841)
SVM with all features Good(0.946) Good(0.814) Excellent(0.984) Good(0.833)
35
36. Answers to research questions
• Question 1: Is it possible to predict fetus health automatically from
Cardiotocography data?
• Answer: Yes, It is possible.
• Question 2: Then how accurate is that model in predicting fetus
health?
• Answer: Based on the performance of models I selected two models
as most implementable
• One of them Random forest with selected features has high overall
accuracy of 95.3%
• Another one Logistic Regression with all features has compromised
overall accuracy of 80.4% to obtain excellent sensitivity in pathologic
of 97%
36
37. Answers to research questions
• Question 3: Which features of cardiotocography data are most
important in predicting fetus health?
• Answer: According to feature importance of random forest model the
feature importance of the five most important features are as below:
Feature Importance
Percentage of time with abnormal short term variability (ASTV) 0.15
Percentage of time with abnormal long term variability (ALTV) 0.13
Histogram mean 0.1
Mean value of short term variability (MSTV) 0.08
Acceleration (AC) 0.07
37
38. How do you know you have become a
Professional in Machine Learning?
• Be a competitions, notebooks, datasets, discussions master in Kaggle. 4x
grandmaster Abhishek Thakur explains:
• https://www.youtube.com/watch?v=z15TKkAPNUM
• Publish the results of your research in an impactful journal.
• Get the Machine learning engineer job you wanted
• Get accepted in your favourite PhD program
• Build a good freelancing portfolio in this field
38
39. Yann Lecun’s Advice
• So my advice is, if you want to get into this, make yourself useful.
• So make a contribution to an open source project, for example.
• Or make an implementation of some standard algorithm that you can't find the
code of
• online, but you'd like to make it available to other people.
• So take a paper that you think is important,
• and then re-implement the algorithm, and then put it open source package,
• or contribute to one of those open source packages.
• And if the stuff you write is interesting and useful, you'll get noticed.
• Maybe you'll get a nice job at a company you really wanted a job at,
• or maybe you'll get accepted in your favorite PhD program or things like this.
39
40. A business example! Digital Marketing!
• A company has passenger ships from Tokyo to Hokkaido
• The company also have a dataset of Internet usage data for it’s
potential customers (Potential Tourists from Tokyo to Hokkaido)
• Can this company deliver custom advertisements based on the users
behavior and characteristics, so that the user is most likely to buy a
ticket to Hokkaido from this company?
40
