Development of a Prediction Algorithm using Boosted Decision Trees for Earlier Diagnoses on Obstructive Sleep Apnea
This research develops a knowledge-based system by using computational intelligent approaches based on Boosting algorithms on decision trees augmented by pruning techniques and Association Rule Mining. This system can provide better prediction accuracies and speedier medical analyses in order to...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2018
|
| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/26594/1/Doreen%20Sim.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This research develops a knowledge-based system by using computational intelligent
approaches based on Boosting algorithms on decision trees augmented by pruning
techniques and Association Rule Mining. This system can provide better prediction
accuracies and speedier medical analyses in order to help medical doctors in the earlier
clinical diagnoses of Obstructive Sleep Apnea, i.e. OSA. The prediction algorithms
developed are based on the OSA datasets collected mainly from the public hospitals in
Selangor, Malaysia. The proposed OSA questionnaires have been newly designed after the
data collection has been completed. The newly proposed and designed OSA questionnaires
are customizable to best fit for Malaysians and have significant differences with the
internationally standardized OSA questionnaires since these questionnaires are tailor-made
based on the raw data collected within populations in Malaysia only. The parameters
involved in the prediction algorithms developed are based on common OSA risk factors
and visual-inspected variables found in the patients’ records in the OSA datasets collected.
As benchmarked comparisons and contrasts, the performance of the computational
intelligent system developed in this research is testified with quite a number of standard
online databases downloaded mainly from the University of California Irvine data
repositories. This is to ensure the generalized performance, flexible strengths and
robustness of the OSA prediction system developed. Research outputs showed that there is
a significant prediction improvement of the algorithms and system developed based on all
types of datasets being accessed against the classical boosting approaches on the same
datasets. There is a stepwise prediction improvement from the classical approach of
Boosted Decision Trees to the developed Boosted Pruned-Decision Trees and then to Boosted Pruned-Association-Rule-Minded-Decision Trees. The developed prediction
iv algorithms have been proven to help medical doctors with earlier clinical diagnoses on OSA cases, especially in Malaysia. |
|---|