ANN-Based Dengue Prediction Model

Abstract

Dengue fever is a substantial global health challenge, demanding early and accurate diagnosis for effective patient care. This paper presents a robust Artificial Neural Network (ANN) approach implemented in the Colab Notebook for Dengue diagnosis. The study's methodology, dataset, implementation details, and results are outlined. The ANN model's superior diagnostic performance compared to traditional methods demonstrates its potential to revolutionize Dengue diagnosis. Dengue fever is a significant global health concern, with millions of cases reported each year. Early and accurate diagnosis is crucial for effective patient management and disease control. This abstract presents a novel approach for dengue diagnosis using Artificial Neural Networks (ANN). Traditional diagnostic methods, such as serological tests and PCR assays, have limitations in terms of cost, time, and accuracy. ANN offers an alternative solution by leveraging its ability to process complex data patterns and make predictions based on a variety of input variables. In this study, we collected clinical, haematological, and demographic data from a cohort of dengue patients and used them as input features for the ANN. The proposed ANN model is trained using a large dataset of dengue cases, which includes various stages of the disease. The neural network is designed to learn and recognize subtle patterns accurately. The ANN's architecture and hyperparameters are optimized to achieve high diagnostic accuracy accurately. The ANN's architecture and hyperparameters are optimized to achieve high diagnostic accuracy. Severe dengue can be life-threatening and requires prompt medical attention. ANNs can potentially address the limitations of existing diagnostic methods by providing more accurate, efficient, and timely diagnoses.