DSpace Repository

Prediction of Typhoid Using Machine Learning and Ann Prior to Clinical Test

Show simple item record

dc.contributor.author Bhuiyan, Md. Atik
dc.contributor.author Rad, Sharaf Shahariare
dc.date.accessioned 2023-04-05T08:24:09Z
dc.date.available 2023-04-05T08:24:09Z
dc.date.issued 23-01-29
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/10143
dc.description.abstract Typhoid fever is a serious and potentially fatal illness that causes a large number of deaths each year, particularly in developing countries. Early and accurate diagnosis is essential for effective treatment and to prevent the spread of the disease. In this study, we used machine learning techniques to develop a predictive model for typhoid fever. We used a dataset of 1746 entries and 29 attributes, and applied ten different algorithms to the data. Our results showed that machine learning can be effective tools for the prediction of typhoid fever, with the XGBoost classifier performing particularly well, achieving an accuracy rate of 97.87%. In addition to the XGBoost classifier, we also evaluated the performance of several other algorithms, including the Random Forest classifier, Extra Trees classifier, and Artificial Neural Network. While each of these classifiers performed well, the XGBoost classifier was found to be the most effective, with accuracy rates of 97.78% and 97.42% for the Random Forest and Extra Trees classifiers, respectively. Overall, our results demonstrate the potential of machine learning and ANNs for the prediction of typhoid fever, and suggest that these technologies could be useful tools for improving the diagnosis and treatment of this disease. Further research will be needed to explore the potential of these technologies in more detail, and to identify the most effective approaches for their implementation and deployment. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Machine learning en_US
dc.subject Typhoid fever en_US
dc.subject Neural networks en_US
dc.subject Treatment en_US
dc.subject Algorithms en_US
dc.subject Technologies en_US
dc.title Prediction of Typhoid Using Machine Learning and Ann Prior to Clinical Test en_US
dc.type Other en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Browse

My Account