Early prediction of dengue outbreak using Artificial Neural Network (ANN) / Muhammad Sirajuddin Ismail

The dengue fever is a mosquito-borne viral infection that has become one of the world's most quickly spreading and dangerous diseases. To prevent and lessen the impact, effective public health management and control tactics require early dengue epidemic detection and forecasting. This study aim...

全面介绍

Saved in:
书目详细资料
主要作者: Ismail, Muhammad Sirajuddin
格式: Thesis
语言:English
出版: 2024
主题:
在线阅读:https://ir.uitm.edu.my/id/eprint/95728/1/95728.pdf
标签: 添加标签
没有标签, 成为第一个标记此记录!
实物特征
总结:The dengue fever is a mosquito-borne viral infection that has become one of the world's most quickly spreading and dangerous diseases. To prevent and lessen the impact, effective public health management and control tactics require early dengue epidemic detection and forecasting. This study aims to investigate the requirements of utilizing the Artificial Neural Network algorithm for prediction of dengue outbreak. The objective is to develop Dengue Outbreak Prediction System using Artificial Neural Network algorithm and evaluate its performance. By employing this algorithm, we can analyse historical dengue data, weather conditions, and other relevant factors these may include temperature, humidity, rainfall, and population density to predict potential outbreak accurately. The developed model holds the potential to assist healthcare industry in findings and predictions that can help raise awareness among community members, empowering them to take preventive measures and participate in vector control efforts. This research contributes to the field by exploring the application of machine learning algorithms in the healthcare industry. The results of this study will provide valuable insights into enhancing the efficiency and reliability of dengue outbreak, ultimately benefiting both the healthcare and people around the world.