DDPM: A Dengue Disease Prediction and Diagnosis Model Using Sentiment Analysis and Machine Learning Algorithms

Abstract

The aedes mosquito-borne dengue viruses cause dengue fever, an arboviral disease (DENVs). In 2019, the World Health Organization forecasts a yearly occurrence of infections from 100 million to 400 million, the maximum number of dengue cases ever testified worldwide, prompting WHO to label the virus one of the world’s top ten public health risks. Dengue hemorrhagic fever can progress into dengue shock syndrome, which can be fatal. Dengue hemorrhagic fever can also advance into dengue shock syndrome. To provide accessible and timely supportive care and therapy, it is necessary to have indispensable practical instruments that accurately differentiate Dengue and its subcategories in the early stages of illness development. Dengue fever can be predicted in advance, saving one’s life by warning them to seek proper diagnosis and treatment. Predicting infectious diseases such as dengue is difficult, and most forecast systems are still in their primary stages. In developing dengue predictive models, data from microarrays and RNA-Seq have been used significantly. Bayesian inferences and support vector machine algorithms are two examples of statistical methods that can mine opinions and analyze sentiment from text. In general, these methods are not very strong semantically, and they only work effectively when the text passage inputs are at the level of the page or the paragraph; they are poor miners of sentiment at the level of the sentence or the phrase. In this research, we propose to construct a machine learning method to forecast dengue fever.

Effectiveness of a diagnostic algorithm for dengue based on an artificial neural network

“…An attempt has been made to approach various techniques of artificial intelligence and machine learning; however, because several variables have been included, the results have been very heterogeneous. 24 Despite this, ANNs have many advantages, including a high capacity to learn and generalize and the ability to handle imprecise, confusing, noisy, and probabilistic information. 25 The published values are heterogeneous due to the type of technology used and the selection of cases and means of confirmation.…”

Determining important features for dengue diagnosis using feature selection methods

“…All fifteen medical doctors interviewed also agree that one of the most important dengue features is fever. Even though only two feature selection methods including FI and PCC chose fever duration as an important feature, fever normally starts 4-10 days after infection and last for 2-7 days [32]. Based on the medical doctors interviewed and medical records collected, temperature also has a significant contribution for dengue prediction that can reach 39-40°C.…”

Contour wavelet diffusion: A fast and high‐quality image generation model

“…The field of diffusion models has seen significant advancements and extensions, such as Deep Diffusion Probabilistic Models (DDPM), 17 Generative Latent Flow Inference (GLIDE), 18 among others. More recently, Diffusion GAN emerged as a novel method that avoids the assumption of unimodal Gaussian distributions by leveraging generative adversarial networks to model complex multimodal distributions.…”