Python as a tool for e-health systems by Diana Pholo

E-health has proven to have many benefits including reduced errors in medical diagnosis.
A number of machine learning (ML) techniques have been applied in medical diagnosis, each
having its benefits and disadvantages.
With its powerful pre-built libraries, Python is great for implementing machine learning in the medical field, where many people do not have an Artificial Intelligence background.

This talk will focus on applying ML on medical datasets using Scikit-learn, a Python module that comes packed with various machine learning algorithms. It will be structured as follows:

An introduction to e-health.
Types of medical data.
Some Benchmark algorithms used in medical diagnosis: Decision trees, K-Nearest Neighbours, Naive Bayes and Support Vector Machines.
How to implement benchmark algorithms using Scikit-learn.
Performance evaluation metrics used in e-health.

This talk is aimed at people interested in real-life applications of machine learning using Python. Although centered around ML in medicine, the acquired skills can be extended to other fields.

About the speaker: Diana Pholo is a PhD student and lecturer in the department of Computer Systems Engineering, at the Tshwane University of Technology.

Transcript

1. Python as a tool for e-health systems By Diana Pholo
2. What is E- health?
3. Types of medical data
4. Algorithms of choice: K-Nearest Neighbour
5. Algorithms of choice: Decision Trees
6. Algorithms of choice: Naïve Bayes
7. Algorithms of choice: Support vector machine
8. Using scikit- learn: Importing Libraries
9. Using scikit- learn: Loading dataset
10. Using scikit- learn: Training models
11. Using scikit- learn: Predicting
12. Cross- validation
13. Using scikit- learn: Evaluating model (1)
14. Using scikit- learn: Evaluating model (2)
15. Concluding notes: Why e- health?