Scopus Indexed Publications

Paper Details

Prediction of Typhoid Using Machine Learning and ANN Prior to Clinical Test
Md. Atik Bhuiyan, Abdullah Islam, Aliza Ahmed Khan, Fatema Tuj Johora, SHARAF SHAHARIARE RAD,

One of the most prevalent illnesses, typhoid causes a large number of fatalities each year, primarily in Africa. A quick and accurate diagnosis is essential in the medical sector. Self-medication, delayed diagnosis, a lack of medical expertise, and inadequate healthcare facilities all contribute to the high incidence of typhoid fever mortality. Machine learning as well as deep learning has worked wonders for extrapolative analysis in the health industry, and as a result, more health industries are utilizing machine learning techniques. This is the earliest evaluation where a typhoid fever prediction model is being developed which predicts prior to a clinical trial. In this paper, deep learning and machine learning have been employed to develop the model. Ten algorithms have been utilized here, and the XGBoost classifier is the best performer with 97.87% accuracy.

"typhoid fever , machine learning , artificial neural network , Xgboost"
Journal or Conference Name
2023 International Conference on Computer Communication and Informatics, ICCCI 2023
Publication Year