Tutorial: Automated Design of Computational Intelligence Techniques for Disease Prediction
Various machine learning, search and image processing techniques have been used to successfully predict diseases. The manual design of these approaches usually require a number of design decisions and can be time-consuming and laborious. Furthermore, more recently prediction from more than one type of data, i.e. multimodal learning, may be necessary to predict diseases. A further challenge with using computational intelligence for disease prediction is imbalanced data. This tutorial focuses on the use of hyper-heuristics and evolutionary algorithms for the automated design of computational intelligence approaches for disease prediction. The tutorial firstly presents an overview of hyper-heuristics and evolutionary algorithms and how these techniques can be used to automate the design of neural networks, genetic programming and image processing techniques for disease prediction. The tutorial will then examine various case studies for disease prediction including the automated design of neural networks, genetic programming and image processing techniques as well as multimodal approaches and approaches catering for data imbalance. The case studies will be presented interactively using a tool developed for the automated design of computational intelligence techniques for disease prediction.
Tutorial Outline
1. Introduction (45 minutes)
1.1 Disease Prediction (Thambo Nyathi)
1.2 Computational Intelligence for Disease Prediction (Thambo Nyathi)
1.3 Multimodal Machine Learning for Disease Prediction (Nelishia Pillay)
1.4 Hyper-heuristics for Automated Design (Nelishia Pillay)
1.5 Evolutionary Algorithms for Automated Design (Thambo Nyathi)
2. Case Studies (60 minutes)
2.1 Genetic Programming (Thambo Nyathi)
Automated design using grammatical evolution
Automated design using genetic algorithms
2.2 Image processing (Thambo Nyathi)
Automated design using genetic programming
Automated design using hyper-heuristics
2.3 Neural Architecture Search (Nelishia Pillay)
Automated design using structured genetic programming
Automated design using hyper-heuristics
2.4 Multimodal Machine Learning (Nelishia Pillay)
Automated design using hyper-heuristics
Automated design using grammatical evolution
3. Discussion and Future Research Directions (Nelishia Pillay and Thambo Nyathi)
Presenters Details
Nelishia Pillay
Department of Computer Science, University of Pretoria, South Africa
E-mail: nelishia.pillay at up.ac.za
Thambo Nyathi
Department of Computer Science, University of Pretoria, South Africa
E-mail: t.nyathi at cs.up.ac.za