Date of Award

Fall 2012

Degree Type

Thesis

Degree Name

Master of Science (MSc)

Department

Computing and Software

Supervisor

Norm Archer

Language

English

Abstract

Medical decision support systems are one of the main applications for data mining and machine learning techniques. Most of these systems involve solving a classification problem. Classification models can be generated by one of two types of learning classification algorithms: batch or incremental learning algorithms.

A batch learning algorithm generates a classification model trained by using the complete available data. Examples of batch learning algorithms are: decision tree C4.5 and multilayer perceptron neural network algorithms. However, an incremental learning algorithm generates a classification model trained incrementally through batches of training data. Examples of this are Learn++ and DWMV Learn++. Incremental learning algorithms are effective in problems in the healthcare domain where the training data become available periodically over time or where the size of database is very large. In the health care system, we consider heart disease a major cause death, and thus, it is a domain requiring attention. Early screening of patients for heart disease before they actually have its symptoms could therefore be an effective solution for decreasing the risk of this disease. Classification techniques can be employed to recognize patients who are at high risk of developing heart disease in order to send them for further attention or treatment by specialists.

This work proposes an incremental learning algorithm, called modified DWMV Learn++, for primary care decision support that classifies patients into high risk and low risk, based on certain risk factors. This system has been tested and proven to have good performance using real-world patient clinical records.

McMaster University Library