| dc.description.abstract | Diabetes is one of deadliest diseases in the world. As per the existing system in Sri Lanka, patients have to visit a diagnostic center, consult their doctor and wait for a day or more to get their result. Moreover, every time they want to get their diagnosis report, they have to waste their money in vain. But with the rise of Machine Learning approaches, we have been able to find a solution to this problem using data mining. Data mining is one of the key areas of Machine learning. It plays a significant role in diabetes research because It has the ability to extract hidden knowledge from a huge amount of diabetes related data. The aim of this research is to develop a system which can predict whether the patient has diabetes or not. Furthermore, predicting the disease early leads to treatment of the patients before it becomes critical. This research has focused on developing a system based on three classification methods namely, Decision Tree, Na?ve Bayes and Support Vector Machine algorithms. Currently, the models give accuracies of 84.6667%, 76.6667%, and 77.3333% for Decision Tree, Na?ve Bayes, and SMO Support Vector Machine respectively. These results have been verified using Receiver Operating Characteristic curves in a cost-sensitive manner. The developed ensemble method uses votes given by the other algorithms to produce the final result. This voting mechanism eliminates the algorithm dependent misclassifications. Results show a significant improvement of accuracy of ensemble method compares to other methods. | en_US |
