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 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.